Вакансии

Стек C#, .NET, PostgreSQL, Redis, Kafka, gRPC, ClickHouse, S3 Вы будете Разрабатывать и поддерживать микросервисную архитектуру.

• Опыт коммерческой разработки на Go от двух лет. • Уверенное знание стандартной библиотеки, механизмов конкурентности.

Project Responsibilities: Design and ship specialist AI agents that use memory, tools, data and multi-step reasoning. Build multi-agent orchestration that routes work between specialist agents, manages dependencies and synthesises conflicting outputs.

Location: United States (Remote) Workplace: Remote ROLE SUMMARY We’re looking for a Software Engineer II to join the Cloud Applications team. This team builds and operates critical parts of the Temporal Cloud customer experience, including Namespaces https://docs.temporal.io/cloud/namespaces, Audit Logs https://docs.temporal.io/cloud/audit-logs, and Export https://docs.temporal.io/cloud/export. Namespaces are the core unit of isolation within Temporal Cloud, providing security boundaries, workflow management, unique identifiers, and dedicated gRPC endpoints. Audit Logs and Export give customers reliable access to the operational and compliance data they depend on. We're looking for an engineer who is excited to learn these systems and contribute to their scalability, reliability, and developer experience. This is a hands-on role where you'll collaborate with experienced engineers, product managers and partner teams to design, build, and operate backend services and data pipelines while growing your distributed-systems expertise. WHAT YOU'LL DO - Design, implement, test, and ship well-scoped features and improvements for Temporal Cloud with support from teammates. - Build and improve services that support namespace provisioning, configuration, scalability, and reliability. - Develop data pipelines that process high-volume usage and operational data across multiple tenants. - Make thoughtful engineering tradeoffs with guidance, balancing data freshness, query cost, tenant isolation, reliability, and operational complexity. - Improve service operability through tests, instrumentation, dashboards, alerts, runbooks, and documentation. - Participate in code reviews, design discussions, incident response, and production debugging. - Collaborate across engineering, product, and customer-facing teams to understand requirements and deliver scoped projects safely. - Improve developer and customer experience through automation, better tooling, and incremental product improvements. WHAT YOU'LL BRING - Experience building and operating production backend systems in a cloud environment. - Proficiency in Go or another applicable backend or systems language, with the ability to ramp up in an existing codebase. - Strong computer science fundamentals, including data structures, concurrency basics, testing, and debugging. - Working knowledge of distributed-systems concepts such as reliability, fault tolerance, consistency, scaling, and failure handling. - Experience with databases, data pipelines, messaging systems, or object storage. - The ability to take a scoped problem, break it into smaller tasks, implement a solution end to end, and incorporate feedback. - An operational mindset, including familiarity with logging, metrics, tracing, deployment, and production troubleshooting. - Comfort collaborating across teams and communicating progress, risks, and technical tradeoffs clearly. - A commitment to correctness, maintainability, and leaving systems better than you found them. NICE TO HAVE - 3+ years of work experience - Experience with Go. - Experience with PostgreSQL, Redshift, Kinesis, Firehose - Experience with S3, Google Cloud Storage, Azure Blob Storage, or other object-storage systems. - Familiarity with Kubernetes and cloud infrastructure. - Experience supporting on-call rotations, investigating incidents, or improving production observability. - Familiarity with Temporal concepts—or a strong interest in learning the platform. TECHNOLOGY STACK - Primary language: Go - Data storage: PostgreSQL, Redshift, S3,Google Cloud Storage, Azure Blob Storage, ClickHouse - Data processing: Kinesis, Firehose - Infrastructure: Kubernetes - Core framework: Temporal You do not need experience with every technology in our stack. We value strong engineering fundamentals and the ability to learn unfamiliar systems. Temporal Technologies is an Equal Opportunity Employer. Temporal Technologies does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status, or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and business need. We embrace and celebrate differences and diversity. Temporal is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. If you need to request a reasonable accommodation, please let your Recruiter know so we can assist.

Location: Cairo, Egypt Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 3+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production. A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft. Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Islamabad, Pakistan Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 3+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production. A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft. Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Almaty, Kazakhstan Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 3+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production. A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft. Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Limassol, Cyprus Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 3+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Help us challenge injustice by creating fair choices for millions of people across in 47 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Annual salary starting from 59 200 EUR gross Final benefits may vary depending on the location.

Location: Cairo, Egypt Workplace: hybrid The Grocery Team is responsible for building and evolving the customer experience for grocery delivery — from discovering stores and products to browsing the catalog, managing the cart, and completing checkout. We’re looking for a QA Engineer to join our team Key Responsibilities Test the full grocery customer journey, including store selection, catalog, search, product details, cart, checkout, payment, and order creation. Validate prices, discounts, inventory, product availability, and store-specific assortment across different locations and partners. Test grocery-specific scenarios such as weighted products, substitutions, unavailable items, quantity limits, and minimum order values. Test integrations between the storefront and catalog, pricing, inventory, order, payment, and delivery services, as well as external grocery partners. Validate fallback and degradation scenarios when a partner or internal service is unavailable. Ensure consistent behavior across mobile applications, WebView, and backend APIs. Develop and maintain automated tests and contribute to API, integration, and end-to-end test coverage. Participate in requirements reviews, test strategy, release validation, and investigation of production issues, taking ownership of quality throughout the product lifecycle. Skills, Knowledge and Expertise Strong experience in functional, integration, API, and end-to-end testing across the full customer journey: catalog, search, PDP, cart, checkout, payment, and order creation. Experience testing dynamic pricing, inventory, discounts, product availability, quantity limits, and minimum order value. Experience testing location- or store-specific assortment, including warehouse and geo-based availability. Good understanding of grocery-specific scenarios, such as weighted products, substitutions, unavailable items, and partial orders. Experience testing integrations with catalog, pricing, inventory, order, payment, and delivery services, including third-party partner APIs. Strong understanding of microservices, event-driven and asynchronous architectures, including retries, timeouts, idempotency, duplicate events, and eventual consistency. Experience with REST and/or gRPC APIs and automated API testing; familiarity with tools such as Postman. Experience creating and maintaining automated tests using frameworks such as Playwright, Cypress, Appium, or similar. Ability to investigate issues using logs, metrics, traces, databases, and message queues; experience with tools such as Grafana, CloudWatch, Kibana, Kafka, and SQL. Experience working with CI/CD pipelines, Docker, and AWS-based environments. Ability to validate consistency across mobile applications, WebView, backend APIs, and distributed services, and troubleshoot partial failures and partner/service outages. Experience contributing to test strategy, requirements reviews, release validation, and production issue analysis, with a focus on business risks and product quality throughout the entire development lifecycle. Nice to have: experience with grocery/e-commerce products, mobile testing, contract testing, external partner integrations, performance/load/resilience testing, feature flags, A/B testing, and product metrics such as conversion, add-to-cart rate, checkout completion, error rate, and latency. Conditions & Benefits Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Islamabad, Pakistan Workplace: hybrid The Grocery Team is responsible for building and evolving the customer experience for grocery delivery — from discovering stores and products to browsing the catalog, managing the cart, and completing checkout. We’re looking for a QA Engineer to join our team Key Responsibilities Test the full grocery customer journey, including store selection, catalog, search, product details, cart, checkout, payment, and order creation. Validate prices, discounts, inventory, product availability, and store-specific assortment across different locations and partners. Test grocery-specific scenarios such as weighted products, substitutions, unavailable items, quantity limits, and minimum order values. Test integrations between the storefront and catalog, pricing, inventory, order, payment, and delivery services, as well as external grocery partners. Validate fallback and degradation scenarios when a partner or internal service is unavailable. Ensure consistent behavior across mobile applications, WebView, and backend APIs. Develop and maintain automated tests and contribute to API, integration, and end-to-end test coverage. Participate in requirements reviews, test strategy, release validation, and investigation of production issues, taking ownership of quality throughout the product lifecycle. Skills, Knowledge and Expertise Strong experience in functional, integration, API, and end-to-end testing across the full customer journey: catalog, search, PDP, cart, checkout, payment, and order creation. Experience testing dynamic pricing, inventory, discounts, product availability, quantity limits, and minimum order value. Experience testing location- or store-specific assortment, including warehouse and geo-based availability. Good understanding of grocery-specific scenarios, such as weighted products, substitutions, unavailable items, and partial orders. Experience testing integrations with catalog, pricing, inventory, order, payment, and delivery services, including third-party partner APIs. Strong understanding of microservices, event-driven and asynchronous architectures, including retries, timeouts, idempotency, duplicate events, and eventual consistency. Experience with REST and/or gRPC APIs and automated API testing; familiarity with tools such as Postman. Experience creating and maintaining automated tests using frameworks such as Playwright, Cypress, Appium, or similar. Ability to investigate issues using logs, metrics, traces, databases, and message queues; experience with tools such as Grafana, CloudWatch, Kibana, Kafka, and SQL. Experience working with CI/CD pipelines, Docker, and AWS-based environments. Ability to validate consistency across mobile applications, WebView, backend APIs, and distributed services, and troubleshoot partial failures and partner/service outages. Experience contributing to test strategy, requirements reviews, release validation, and production issue analysis, with a focus on business risks and product quality throughout the entire development lifecycle. Nice to have: experience with grocery/e-commerce products, mobile testing, contract testing, external partner integrations, performance/load/resilience testing, feature flags, A/B testing, and product metrics such as conversion, add-to-cart rate, checkout completion, error rate, and latency. Conditions & Benefits Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Limassol, Cyprus Workplace: hybrid The Grocery Team is responsible for building and evolving the customer experience for grocery delivery — from discovering stores and products to browsing the catalog, managing the cart, and completing checkout. We’re looking for a QA Engineer to join our team Key Responsibilities Test the full grocery customer journey, including store selection, catalog, search, product details, cart, checkout, payment, and order creation. Validate prices, discounts, inventory, product availability, and store-specific assortment across different locations and partners. Test grocery-specific scenarios such as weighted products, substitutions, unavailable items, quantity limits, and minimum order values. Test integrations between the storefront and catalog, pricing, inventory, order, payment, and delivery services, as well as external grocery partners. Validate fallback and degradation scenarios when a partner or internal service is unavailable. Ensure consistent behavior across mobile applications, WebView, and backend APIs. Develop and maintain automated tests and contribute to API, integration, and end-to-end test coverage. Participate in requirements reviews, test strategy, release validation, and investigation of production issues, taking ownership of quality throughout the product lifecycle. Skills, Knowledge and Expertise Experience with grocery/e-commerce products Experience testing dynamic pricing, inventory, discounts, product availability, quantity limits, and minimum order value. Good understanding of grocery-specific scenarios, such as weighted products, substitutions, unavailable items, and partial orders. Experience testing integrations with catalog, pricing, inventory, order, payment, and delivery services, including third-party partner APIs. Experience with REST and/or gRPC APIs and automated API testing; familiarity with tools such as Postman. Experience creating and maintaining automated tests using frameworks such as Playwright, Cypress, Appium, or similar. Ability to validate consistency across mobile applications, WebView, backend APIs, and distributed services, and troubleshoot partial failures and partner/service outages. Nice to have: contract testing, external partner integrations, performance/load/resilience testing, feature flags, A/B testing, and product metrics such as conversion, add-to-cart rate, checkout completion, error rate, and latency. Why join us Help us challenge injustice by creating fair choices for millions of people across 47 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.

Location: Belgrade, Remote Workplace: remote Tabby creates financial freedom in the way people shop, earn and save by reshaping their relationship with money. Over 25 million users choose Tabby to stay in control of their spending and make the most out of their money. The company’s flagship offering allows shoppers to split their payments online and in-store with no interest or fees. Over 70,000 global brands and small businesses, including Amazon, Noon, IKEA, and SHEIN use Tabby to accelerate growth and gain loyal customers by offering easy and flexible payments online and in stores. Tabby generates over $18 billion in annual transaction volume for its partner brands and is the highest-rated, most-reviewed, largest, and fastest-growing FinTech in the GCC region. Tabby launched in 2019 and has since raised +$1 billion in equity and debt funding from global and regional investors, and is now valued at $6,5 billion. About the role We are looking for a Senior Product Manager with a strong business and technical background to join our Statements team in the Core Platform Domain. The team owns the statement and billing engine behind Tabby’s card products. You will be the Billing team’s go-to partner for Tabby Card. You’ll own all billing functionality the card needs, advise on what’s feasible from a platform, financial and compliance perspective, and shape the rules and architecture behind new card products and markets. This is a highly cross-functional role, great for someone who has built or run card billing, card management or ledger platforms and wants to own how a credit product works under the hood. What you will be doing Partner with Tabby Card product teams on all billing functionality and advise on platform, financial and compliance feasibility Own the billing and statements roadmap across existing and next-generation card products Define the core billing mechanics (statement cycles, minimum payment, repayment rules, installment plans and refunds) and shape the underlying data model and architecture with engineering Translate regulatory and accounting requirements into product requirements with Compliance and Finance Align scope and dependencies with adjacent teams: lending, risk, collections, support and finance Prioritize the backlog and support engineering and QA on requirements and edge cases What we are looking for 3+ years in product management with a strong focus on LMS, billing, core banking, lending, or ledger platforms Experience with platform products in a regulated financial environment Upper-Intermediate English or higher Strong at turning complex business and regulatory rules into precise, implementation-ready specs Hands-on with SQL and product architecture: comfortable designing data models, service interactions and API contracts with engineers Active use of AI tools in daily work, such as prototyping, data analysis, spec writing and automating routine tasks Good understanding of API and integration design: REST, gRPC Strong plus Experience in fintech, payments, banking, wallets, cards, or ledger/accounting domains Familiarity with platforms such as Thought Machine, Mambu, Galileo, Marqeta or TSYS Understanding of double-entry accounting and reconciliation What success looks like In this role, success means you can: Turn ambiguous financial or product requirements into clear, scalable platform designs Help teams implement changes safely in critical money movement systems Improve consistency across products by designing reusable CoreBank capabilities Grow from owning requirements into shaping the direction and evolution of the platform Benefits Locations We hire candidates based within four hours of Dubai time , with remote work available in eligible locations. Depending on your circumstances, we can also discuss an individual relocation plan to our hubs in Armenia, Georgia, Serbia, Poland, and Spain. What you can expect Flexible working hours, coordinated with your team and the needs of your role. Autonomy and responsibility from day one. Opportunities to grow through the quality and impact of your work. Participation in our employee stock option program. We’re committed to an inclusive, high-performing workplace where people from different backgrounds have the support to grow and achieve their goals. If this sounds like the right opportunity for you, we’d love to hear from you.

Location: European Union What you'll be solving A pod is mid-request. The node it's running on is about to disappear - Spot is pulling the instance, or Autoscaler has flagged the node as underutilized and marked it for removal. The safe move is to wait. The profitable move is to act now. Every team in this pipeline is built to close that gap: make the aggressive call and still guarantee nothing breaks. That guarantee is the hard part. Deciding which nodes to kill, which pods to move, and which storage volume to resize is a genuinely hard optimization problem - several of them are P=NP-hard in the general case - and it has to run continuously, in production, faster than a human would ever attempt it by hand. Get it right and clusters run at half the cost. Get it wrong once and you've taken down something live. This posting covers multiple teams working on different pieces of the same automation problem node placement algorithms and provisioning, deep integrations with tools like Karpenter, workload rightsizing (vertical and horizontal), moving running workloads between nodes without interrupting them, storage that scales itself, and GPU capacity optimization across clouds and regions. All of it runs in real time, in production, with no human tuning YAML in the loop. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Cyprus, Malta, Finland, Sweden, Ireland, Moldova, North Macedonia, Montenegro, Slovenia, and Ukraine. Requirements: Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices - not just make something work. Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. Understanding of Kubernetes internals – autoscaling and networking. You've personally driven a complex project end-to-end. Experience with AI coding agents and agentic development workflows. You've used observability tooling in production. CI/CD and DevOps practices experience. Strong English skills, both verbal and written. Startup mindset: adaptable, proactive, and comfortable with ambiguity. Nice-to-haves: Deep hands-on experience with cloud platforms (AWS, GCP, or Azure) – including real understanding of how compute, networking, and storage work under the hood. Deep knowledge of EKS, GKE, or AKS internals. Container runtime experience CRI-O, runc, containerd. Cloud storage depth – block storage, CSI, volume management, filesystem internals. Low-level Linux experience: process internals, packet-level networking (NAT, iptables, conntrack, eBPF, SDN), filesystems, storage. Low-level systems programming experience in C or C++. Kubernetes or cloud-native OSS contributions. Experience with AI coding agents and agentic development workflows. Responsibilities: Design and build distributed systems that operate Kubernetes infrastructure autonomously at scale. Write production Go services that interact with AWS, GCP, and Azure APIs for real-time cloud resource management. Own features end-to-end: from design through implementation, testing, and production rollout (most projects ship in 1-4 weeks). Debug complex production issues across cloud providers, Kubernetes clusters, and distributed services. Collaborate with product and other engineering teams to solve problems that don't have textbook solutions. Work with time-series data, cloud provider APIs, and Kubernetes control plane internals. Contribute to product direction and backlog - shape what gets built next, not just how it's built. Participate in on-call rotation for teams that require it. Tools we use daily: Programming Languages: Go Cloud & Orchestration: Kubernetes, AWS, GCP, Azure Infrastructure as Code: Terraform Databases & Storage: PostgreSQL, Cloud Object Storage, ClickHouse Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs Observability: Prometheus, Grafana, Loki, Tempo CI/CD & GitOps: GitLab/Github CI with ArgoCD and Kargo What’s in it for you? Competitive salary (€6,500 - €9,000 gross, depending on the level of experience) Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive *As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. *Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Cyprus; Finland; Ireland; Malta; Moldova; North Macedonia; Sweden What you'll be solving A pod is mid-request. The node it's running on is about to disappear - Spot is pulling the instance, or Autoscaler has flagged the node as underutilized and marked it for removal. The safe move is to wait. The profitable move is to act now. Every team in this pipeline is built to close that gap: make the aggressive call and still guarantee nothing breaks. That guarantee is the hard part. Deciding which nodes to kill, which pods to move, and which storage volume to resize is a genuinely hard optimization problem - several of them are P=NP-hard in the general case - and it has to run continuously, in production, faster than a human would ever attempt it by hand. Get it right and clusters run at half the cost. Get it wrong once and you've taken down something live. This posting covers multiple teams working on different pieces of the same automation problem node placement algorithms and provisioning, deep integrations with tools like Karpenter, workload rightsizing (vertical and horizontal), moving running workloads between nodes without interrupting them, storage that scales itself, and GPU capacity optimization across clouds and regions. All of it runs in real time, in production, with no human tuning YAML in the loop. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Cyprus, Malta, Finland, Sweden, Ireland, Moldova, North Macedonia, Montenegro, Slovenia, and Ukraine. Requirements: Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices - not just make something work. Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. Understanding of Kubernetes internals – autoscaling and networking. You've personally driven a complex project end-to-end. Experience with AI coding agents and agentic development workflows. You've used observability tooling in production. CI/CD and DevOps practices experience. Strong English skills, both verbal and written. Startup mindset: adaptable, proactive, and comfortable with ambiguity. Nice-to-haves: Deep hands-on experience with cloud platforms (AWS, GCP, or Azure) – including real understanding of how compute, networking, and storage work under the hood. Deep knowledge of EKS, GKE, or AKS internals. Container runtime experience CRI-O, runc, containerd. Cloud storage depth – block storage, CSI, volume management, filesystem internals. Low-level Linux experience: process internals, packet-level networking (NAT, iptables, conntrack, eBPF, SDN), filesystems, storage. Low-level systems programming experience in C or C++. Kubernetes or cloud-native OSS contributions. Experience with AI coding agents and agentic development workflows. Responsibilities: Design and build distributed systems that operate Kubernetes infrastructure autonomously at scale. Write production Go services that interact with AWS, GCP, and Azure APIs for real-time cloud resource management. Own features end-to-end: from design through implementation, testing, and production rollout (most projects ship in 1-4 weeks). Debug complex production issues across cloud providers, Kubernetes clusters, and distributed services. Collaborate with product and other engineering teams to solve problems that don't have textbook solutions. Work with time-series data, cloud provider APIs, and Kubernetes control plane internals. Contribute to product direction and backlog - shape what gets built next, not just how it's built. Participate in on-call rotation for teams that require it. Tools we use daily: Programming Languages: Go Cloud & Orchestration: Kubernetes, AWS, GCP, Azure Infrastructure as Code: Terraform Databases & Storage: PostgreSQL, Cloud Object Storage, ClickHouse Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs Observability: Prometheus, Grafana, Loki, Tempo CI/CD & GitOps: GitLab/Github CI with ArgoCD and Kargo What’s in it for you? Competitive salary (€6,500 - €9,000 gross, depending on the level of experience) Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive *As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. *Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Estonia; Finland; Greece; Latvia; Romania; Sweden Why Cast AI? Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production. The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation. Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS. Global team, diverse perspectives We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. Unicorn momentum In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started. Join us as we build the future of autonomous infrastructure. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Cyprus, Malta, Finland, Sweden, Ireland, Moldova, North Macedonia, Montenegro, Slovenia, and Ukraine. About the role A team ships a new service, and getting it from a laptop to production - built, tested, deployed, and visible when something goes wrong - shouldn't require that team to become infrastructure experts. It should be a paved road: a CI/CD pipeline that just works, observability that's there by default, and standard ways of doing things that are the obvious best choice, because the alternative is every team reinventing the same solutions on its own. The Platform team builds and maintains that road, and the work is internal - the users are Cast AI's own engineers. The team is a multiplier for the engineering organization: whatever it builds is built once and serves every team, and the time product teams would otherwise spend on infrastructure goes back into developing their products. What the team owns is whether the tools engineers use to build, deploy, and observe their services are good enough that reliability is actually achievable, and with hundreds of engineers and dozens of services leaning on that at once, the platform has to be fast, correct, and boring in the best sense. Requirements: Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices - not just make something work. Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. Experience in platform engineering - you've built and operated (not just consumed) most, but not necessarily all of: CI/CD and GitOps systems (GitLab CI, Argo CD, Kargo), infrastructure as code (Terraform), container orchestration (Kubernetes, Helm), observability and monitoring tooling (VictoriaMetrics, Grafana, Loki, Tempo), and cloud platforms (AWS, GCP, or Azure). Experience with AI coding agents and agentic development workflows. You've personally driven a complex project end-to-end. Strong debugging, optimization, and performance-tuning skills. Strong English skills, both verbal and written. Startup mindset: adaptable, proactive, and comfortable with ambiguity. Participate in on-call rotation for teams that require it. Nice to have: Kubernetes operators, Helm chart authoring, admission webhooks. Internal developer platform (IDP) design - golden paths, self-service tooling, service catalogs. Local development environment tooling (devcontainers, Tilt, Skaffold, or similar). Kubernetes or cloud-native OSS contributions. Responsibilities: Design, build, and operate the CI/CD pipelines every engineering team ships through. Build and maintain the observability and monitoring tooling other teams use to debug their own systems in production. Build local development environment tooling that lets engineers reproduce production-like conditions on their laptop. Reduce friction for every other team: identify where engineers lose time to infrastructure and fix it at the root, not with a workaround. Debug complex issues that span CI/CD, observability tooling, Kubernetes, and cloud infrastructure. Contribute to the platform roadmap and backlog - shape what gets built next for the engineers who depend on you. Tools we use daily: Programming Language: Go Cloud & Orchestration: Kubernetes, GCP, AWS, Azure Infrastructure as Code: Terraform Databases & Storage: ClickHouse, Cloud Object Storage Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs Observability: Grafana, VictoriaMetrics, VictoriaLogs, VictoriaTraces, Loki, Tempo CI/CD & GitOps: GitLab CI, ArgoCD, Kargo What’s in it for you? Competitive salary (€6,500 – €9,000 gross, depending on the level of experience) plus separate compensation for on-call participation (passive coverage and active interventions) Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development – including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. You'll be expected to travel to Vilnius for onboarding - travel costs are covered by the company. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive *As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. *Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Cyprus; Ireland; Malta; Moldova; Montenegro; North Macedonia; Ukraine Why Cast AI? Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production. The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation. Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS. Global team, diverse perspectives We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. Unicorn momentum In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started. Join us as we build the future of autonomous infrastructure. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Cyprus, Malta, Finland, Sweden, Ireland, Moldova, North Macedonia, Montenegro, Slovenia, and Ukraine. About the role A team ships a new service, and getting it from a laptop to production - built, tested, deployed, and visible when something goes wrong - shouldn't require that team to become infrastructure experts. It should be a paved road: a CI/CD pipeline that just works, observability that's there by default, and standard ways of doing things that are the obvious best choice, because the alternative is every team reinventing the same solutions on its own. The Platform team builds and maintains that road, and the work is internal - the users are Cast AI's own engineers. The team is a multiplier for the engineering organization: whatever it builds is built once and serves every team, and the time product teams would otherwise spend on infrastructure goes back into developing their products. What the team owns is whether the tools engineers use to build, deploy, and observe their services are good enough that reliability is actually achievable, and with hundreds of engineers and dozens of services leaning on that at once, the platform has to be fast, correct, and boring in the best sense. Requirements: Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices - not just make something work. Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. Experience in platform engineering - you've built and operated (not just consumed) most, but not necessarily all of: CI/CD and GitOps systems (GitLab CI, Argo CD, Kargo), infrastructure as code (Terraform), container orchestration (Kubernetes, Helm), observability and monitoring tooling (VictoriaMetrics, Grafana, Loki, Tempo), and cloud platforms (AWS, GCP, or Azure). Experience with AI coding agents and agentic development workflows. You've personally driven a complex project end-to-end. Strong debugging, optimization, and performance-tuning skills. Strong English skills, both verbal and written. Startup mindset: adaptable, proactive, and comfortable with ambiguity. Participate in on-call rotation for teams that require it. Nice to have: Kubernetes operators, Helm chart authoring, admission webhooks. Internal developer platform (IDP) design - golden paths, self-service tooling, service catalogs. Local development environment tooling (devcontainers, Tilt, Skaffold, or similar). Kubernetes or cloud-native OSS contributions. Responsibilities: Design, build, and operate the CI/CD pipelines every engineering team ships through. Build and maintain the observability and monitoring tooling other teams use to debug their own systems in production. Build local development environment tooling that lets engineers reproduce production-like conditions on their laptop. Reduce friction for every other team: identify where engineers lose time to infrastructure and fix it at the root, not with a workaround. Debug complex issues that span CI/CD, observability tooling, Kubernetes, and cloud infrastructure. Contribute to the platform roadmap and backlog - shape what gets built next for the engineers who depend on you. Tools we use daily: Programming Language: Go Cloud & Orchestration: Kubernetes, GCP, AWS, Azure Infrastructure as Code: Terraform Databases & Storage: ClickHouse, Cloud Object Storage Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs Observability: Grafana, VictoriaMetrics, VictoriaLogs, VictoriaTraces, Loki, Tempo CI/CD & GitOps: GitLab CI, ArgoCD, Kargo What’s in it for you? Competitive salary (€6,500 – €9,000 gross, depending on the level of experience) plus separate compensation for on-call participation (passive coverage and active interventions) Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development – including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. You'll be expected to travel to Vilnius for onboarding - travel costs are covered by the company. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive *As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. *Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Croatia; Cyprus; Estonia; Greece; Ireland; Malta; Romania Why Cast AI? Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production. The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation. Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS. Global team, diverse perspectives We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. Unicorn momentum In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started. Join us as we build the future of autonomous infrastructure. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, and Ukraine. What you'll be solving Every dollar Cast AI saves a customer has to be provable, every action taken on their infrastructure has to be attributable to someone authorized to take it, and every price used to make that case has to be current across every cloud, region, and availability zone. This is the trust and accounting layer underneath the whole platform: get it wrong, and customers can't log in, can't trust the savings number, or get billed incorrectly - any one of which ends the relationship. This posting covers multiple teams working on different pieces of the same trust-and-accounting problem: authentication, authorization, and SSO for every user and workload. Billing, audit trails, notifications, and feature flags that keep the platform transparent for every customer. Real-time cloud pricing tracking across every region and availability zone, including Spot availability and discounts. And reporting that turns high-volume time-series data into fast, accurate answers about what the automation actually saved. Requirements Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices - not just make something work. Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. You've built or operated trust-critical systems at scale - authentication/authorization, billing, or financial/accounting-adjacent data, where being wrong has real consequences. Good understanding of API design best practices - versioning, backward compatibility, auth, idempotency, async patterns, and similar. Strong debugging, optimization, and performance-tuning skills – including query profiling, index design, and database performance tuning beyond ORM usage. Experience operating and optimizing systems under high load - you know how to find and fix the bottleneck, not just add more resources. Hands-on experience with Kubernetes - you understand the core concepts (pods, services, deployments) and have deployed or debugged something on a cluster. Experience with AI coding agents and agentic development workflows. You've personally driven a complex project end-to-end. Strong English skills, both verbal and written. Startup mindset: adaptable, proactive, and comfortable with ambiguity. Participate in on-call rotation for teams that require it. Nice to haves Multi-tenant identity system design - authentication, authorization, SSO. Enterprise identity protocols - SAML, OIDC, and SCIM for enterprise account provisioning and sync. Billing, audit-trail, notification, or feature-flag systems in production. Experience with cloud provider pricing/billing APIs across AWS, GCP, or Azure, or tracking Spot instance availability. High-cardinality time-series ingestion and querying at scale. Understanding of Kubernetes internals. CI/CD and DevOps practices experience. Observability tooling (Prometheus, Grafana, OpenTelemetry) in production. Responsibilities Design, build, and operate backend services in Go that other teams and customers depend on being correct. Own features end-to-end: from design through implementation, testing, and production rollout. Debug complex production issues involving security-, money-, or trust-critical flows. Collaborate with product and other engineering teams to solve problems that don't have textbook solutions. Contribute to product direction and backlog - shape what gets built next, not just how it's built. Tools we use daily Programming Language: Go Cloud & Orchestration: Kubernetes, AWS, GCP, Azure Infrastructure as Code: Terraform Databases & Storage: PostgreSQL, Cloud Object Storage, ClickHouse Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs Observability: Prometheus, Grafana, Loki, Tempo CI/CD & GitOps: GitLab CI with ArgoCD What’s in it for you? Competitive salary (€6,500 – €9,000 gross, depending on the level of experience) plus separate compensation for on-call participation (passive coverage and active interventions) Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development – including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. You'll be expected to travel to Vilnius for onboarding - travel costs are covered by the company. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive *As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. *Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Moldova; Montenegro; Slovenia; Ukraine Why Cast AI? Cast AI is an automation platform that operates cloud-native and AI infrastructure at scale. By embedding autonomous decision-making directly into Kubernetes and cloud environments, Cast AI continuously optimizes performance, reliability, and efficiency in production. The old way doesn't work. As Kubernetes and AI environments grow, manual decisions don’t. Cast AI replaces tickets, alerts, and manual tuning with continuous automation that adapts infrastructure as conditions change. Efficiency and cost savings follow naturally from that automation. Over 2,100 companies already rely on Cast AI, including Akamai, BMW, Cisco, FICO, HuggingFace, NielsenIQ, Swisscom, and TGS. Global team, diverse perspectives We're headquartered in Miami, but our impact is international. We take a global and intentional approach to diversity. Today, Cast AI operates across 34 countries spanning Europe, North America, Latin America, and APAC, bringing a wide range of perspectives into how we build and lead. Unicorn momentum In January 2026, we achieved unicorn status with a strategic investment from Pacific Alliance Ventures, the corporate venture arm of Shinsegae Group (a $50+ billion Korean conglomerate). Our valuation now exceeds $1 billion, and we're just getting started. Join us as we build the future of autonomous infrastructure. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia, Cyprus, Malta, Finland, Sweden, Ireland, Moldova, North Macedonia, Montenegro and Ukraine. What you'll be solving Every dollar Cast AI saves a customer has to be provable, every action taken on their infrastructure has to be attributable to someone authorized to take it, and every price used to make that case has to be current across every cloud, region, and availability zone. This is the trust and accounting layer underneath the whole platform: get it wrong, and customers can't log in, can't trust the savings number, or get billed incorrectly - any one of which ends the relationship. This posting covers multiple teams working on different pieces of the same trust-and-accounting problem: authentication, authorization, and SSO for every user and workload. Billing, audit trails, notifications, and feature flags that keep the platform transparent for every customer. Real-time cloud pricing tracking across every region and availability zone, including Spot availability and discounts. And reporting that turns high-volume time-series data into fast, accurate answers about what the automation actually saved. Requirements Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices - not just make something work. Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. You've built or operated trust-critical systems at scale - authentication/authorization, billing, or financial/accounting-adjacent data, where being wrong has real consequences. Good understanding of API design best practices - versioning, backward compatibility, auth, idempotency, async patterns, and similar. Strong debugging, optimization, and performance-tuning skills – including query profiling, index design, and database performance tuning beyond ORM usage. Experience operating and optimizing systems under high load - you know how to find and fix the bottleneck, not just add more resources. Hands-on experience with Kubernetes - you understand the core concepts (pods, services, deployments) and have deployed or debugged something on a cluster. Experience with AI coding agents and agentic development workflows. You've personally driven a complex project end-to-end. Strong English skills, both verbal and written. Startup mindset: adaptable, proactive, and comfortable with ambiguity. Participate in on-call rotation for teams that require it. Nice to haves Multi-tenant identity system design - authentication, authorization, SSO. Enterprise identity protocols - SAML, OIDC, and SCIM for enterprise account provisioning and sync. Billing, audit-trail, notification, or feature-flag systems in production. Experience with cloud provider pricing/billing APIs across AWS, GCP, or Azure, or tracking Spot instance availability. High-cardinality time-series ingestion and querying at scale. Understanding of Kubernetes internals. CI/CD and DevOps practices experience. Observability tooling (Prometheus, Grafana, OpenTelemetry) in production. Responsibilities Design, build, and operate backend services in Go that other teams and customers depend on being correct. Own features end-to-end: from design through implementation, testing, and production rollout. Debug complex production issues involving security-, money-, or trust-critical flows. Collaborate with product and other engineering teams to solve problems that don't have textbook solutions. Contribute to product direction and backlog - shape what gets built next, not just how it's built. Tools we use daily Programming Language: Go Cloud & Orchestration: Kubernetes, AWS, GCP, Azure Infrastructure as Code: Terraform Databases & Storage: PostgreSQL, Cloud Object Storage, ClickHouse Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs Observability: Prometheus, Grafana, Loki, Tempo CI/CD & GitOps: GitLab CI with ArgoCD What’s in it for you? Competitive salary (€6,500 – €9,000 gross, depending on the level of experience) plus separate compensation for on-call participation (passive coverage and active interventions) Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Equity options. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development – including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. You'll be expected to travel to Vilnius for onboarding - travel costs are covered by the company. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive *As part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. *Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote