Вакансии

Location: Москва Мы занимаемся сопровождением автоматизированных систем, обеспечивающих сбор, хранение и анализ данных о контрагентах банка для оценки их надежности и предотвращения рисков. Наши инструменты помогают банку оперативно выявлять угрозы экономической безопасности и минимизировать возможные убытки. Обязанности * участие в приемо-сдаточных испытаниях, внедрение релизов на промышленный контур * решение обращений пользователей (инциденты и запросы на обслуживание) * настройка инфраструктурного и прикладного мониторинга * установка обновлений на сервера (ОС, СУБД) * участие в проведении нагрузочного тестирования * ведение базы знаний, подготовка инструкций для дежурной смены. **Технологический стек:** * ОС, приложения и системы хранения: SberLinux, Openshift, PostgreSQL * DevOps: Jenkins, Git, Ansible * мониторинг: Zabbix+Grafana, Dynatrace (Рефлекс). Требования * высшее техническое образование (возможно студенты старших курсов заочного обучения) * понимание архитектуры современных автоматизированных систем/приложений. * опыт администрирования/сопровождения прикладных автоматизированных систем * уверенное знание SQL и опыт работы с современноми СУБД * понимание основ ITSM/ITIL * навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов * инструментальное владение AI для анализа, генерации и автоматизации. **Будет плюсом:** * опыт сопровождения высоконагруженных автоматизированных систем * опыт работы в технической поддержке крупной компании или банке Условия * комфортный современный офис рядом м. Ленинский проспект, Вавилова 23 * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте (для Junior позиций) * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * вознаграждение за рекомендацию друзей в команду Сбера.

Задачи : -Примерно половина времени будет посвящена разработке, вторая половина — поддержанию скорости, безопасности и качества всей системы. -Создавать функции целиком: схема данных, API, фоновые задачи, интерфейс и запуск в продакшене.

Принимать обращения клиентов в рабочих чатах, по электронной почте и телефону, регистрировать их в системе учёта. Уточнять симптомы, собирать техническую информацию и определять влияние проблемы на работу клиента.

Публичное API: Развивать OpenAI/Anthropic-совместимое API, API медиамоделей и транскрибации. Отвечать за стабильность, идемпотентность и мониторинг.

Разрабатывать и поддерживать Ansible playbook’и, шаблоны конфигураций и docker-compose файлы. Внедрять практики IaC для управления инфраструктурой.

Location: Canada; United States Workplace: Remote ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ THE ROLE Docker Hardened Images (DHI) is Docker's catalogue of security-hardened, enterprise-grade container images and Helm charts, built to be minimal, up-to-date, and safe to deploy in regulated and security-conscious environments. We're looking for a Principal Software Engineer to set the long-term technical direction for this catalogue and shape how the broader container security ecosystem evolves inside and beyond Docker. At this level, you'll work across organizations, not just within one team. You'll set direction for major domains, resolve the highest-ambiguity problems in container supply chain security, and grow your impact through architectural leadership, cross-functional alignment, and influence on industry standards. This is a pure individual contributor role; influence comes through technical vision, design, and the ability to move organizations. _______________________________________________________________________ RESPONSIBILITIES - Own the architecture for DHI as a platform, making high-leverage decisions that define how images and Helm charts are authored, secured, distributed, and evolved across the entire catalogue and across Docker's broader product surface. - Define and drive multi-quarter technical roadmaps for the container hardening domain, balancing upstream velocity, supply chain security posture, customer compliance requirements, and long-term catalogue scalability. - Identify systemic patterns from customer, operational, and upstream signals and translate them into architectural changes that span the catalogue, not just individual images or charts. - Build alignment across teams and executives on technical strategy, tradeoffs, and sequencing, including product, security, infrastructure, and customer-facing functions. - Represent Docker as a trusted technical voice in escalations, key enterprise customer engagements, and upstream OSS communities on issues that shape the future of secure container delivery. - Set conventions and standards at scale, defining the patterns others build within for image definition, Helm chart adaptation, hardening strategy, and supply chain tooling (Sigstore, SBOM, SLSA), and evolving those standards as the ecosystem changes. - Drive the hardest, most ambiguous packaging problems where the right answer requires reasoning across upstream dynamics, security tradeoffs, multi-arch constraints, and customer impact simultaneously. - Shape the integration test infrastructure and developer tooling strategy so that DHI's engineering platform scales with the catalogue and raises quality across the team. - Mentor and raise the technical ceiling for staff and senior engineers across the org, through review, design, and well-chosen architectural decisions that create durable leverage. - Take part in the paid on-call rotation; respond to incidents, debug production issues, and drive systemic improvements in reliability. _______________________________________________________________________ QUALIFICATIONS - 10+ years of backend engineering experience, including extensive work on production-grade, distributed systems at scale. - Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience. - Ability to set multi-quarter technical roadmaps and align stakeholders (engineering, product, and executives) on strategy and tradeoffs. - Deep expertise in the container and Kubernetes ecosystem: you have strong, grounded opinions about cert-manager, kyverno, grafana, istio, and similar projects, and you can reason about tradeoffs at the ecosystem level, not just the image level. - Mastery of container supply chain security concepts (provenance, attestation, SBOM, signing, SLSA) and experience driving posture decisions across an organization rather than implementing them on a single project. - Strong software engineering fundamentals: code review, testing, source control, CI/CD, and Go sufficient to shape infrastructure and harness design. - Track record of technical influence without authority across multiple teams or organizations, raising quality through design docs, standards, review, and mentorship. - Experience navigating upstream OSS communities as a decision-maker, representing a downstream organization's requirements and shaping upstream direction on security-relevant issues. - Comfort working across remote, distributed teams and communicating complex technical strategy clearly to both technical and non-technical audiences. BONUS BUT NOT REQUIRED - Experience as a package maintainer at a Linux distribution, Homebrew, or comparable ecosystem. - Hands-on experience implementing or operationalizing supply chain tooling (Sigstore, SBOM, SLSA) at org scale. - Experience in regulated environments (FedRAMP, FIPS, PCI) with direct exposure to compliance requirements shaping engineering decisions. - Prior Principal or Distinguished IC experience on a platform, security, or developer-tools team. - Experience engaging directly with enterprise customers on container security architecture. _______________________________________________________________________ WHAT TO EXPECT FIRST 30 DAYS - Align with leadership on the most critical org-wide technical risks and opportunities in container supply chain security. - Develop a point of view on DHI's current architectural constraints and where the highest-leverage interventions are. - Begin mapping the cross-functional landscape: product priorities, upstream pressures, customer compliance signals, and engineering gaps. FIRST 90 DAYS - Drive an architectural decision that unblocks multiple teams and reduces systemic risk across the catalogue. - Establish a feedback loop from customer, operational, and upstream signals into the multi-quarter roadmap. - Engage upstream OSS communities on at least one DHI-relevant issue with meaningful influence on direction. FIRST YEAR - Deliver a major platform or standards evolution with broad adoption across DHI and adjacent teams. - Create durable alignment across engineering, product, and security on catalogue architecture, supply chain posture, and hardening strategy. - Raise the technical ceiling for the engineers around you, measurably improving review quality, architectural consistency, and the team's ability to operate independently at a higher level. Visa sponsorship: Docker considers visa sponsorship on a case-by-case basis based on business needs. _______________________________________________________________________ COMPENSATION & EQUITY All full-time roles include equity. Final compensation is determined on factors including but not limited to experience, skills, qualifications, and location. Canada: CA$245,913 – CA$397,139 base United States: $180,500 – $291,500 base ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE

Location: New York ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. The Role We are seeking highly experienced Site Reliability Engineers (SRE) to shape the reliability, scalability and performance of our platform and customer facing applications. You will work closely with our software engineers and research teams to ensure our systems meet and exceed our internal and external customers' expectations.     What You Will Do As a Site Reliability Engineer, you balance the day-to-day operations on production systems with long-term software engineering improvements to reduce operational toil and foster the reliability, availability, and performance of these systems.   Operations • Design, build, and maintain scalable, highly available and fault-tolerant infrastructures to support our web services and ML workloads • Make sure our platform, inference and model training environments are always highly available and enable seamless replication of work environments across several HPC clusters • Operate systems and troubleshoot issues in production environments (interrupts, on-call responses, users admin, data extraction, infrastructure scaling, etc.) • Implement and improve monitoring, alerting, and incident response systems to ensure optimal system performance and minimize downtime • Implement and maintain workflows and tools (CI/CD, containerization, orchestration, monitoring, logging and alerting systems) for both our client-facing APIs and large training runs • Participate occasionally in on-call rotations to respond to incidents and perform root cause analysis to prevent future occurrences   Development • Drive continuous improvement in infrastructure automation, deployment, and orchestration using tools like Kubernetes, Flux, Terraform • Collaborate with AI/ML researchers to develop and implement solutions that enable safe and reproducible model-training experiments • Build a cloud-agnostic platform offering an abstraction layer between science and infrastructure • Design and develop new workflows and tooling to improve to the reliability, availability and performance of our systems (automation scripts, refactoring, new API-based features, web apps, dashboards, etc.) • Collaborate with the security team to ensure infrastructure adheres to best security practices and compliance requirements • Document processes and procedures to ensure consistency and knowledge sharing across the team • Contribute to open-source projects, research publications, blog articles and conferences   What We're Looking For • Master’s degree in Computer Science, Engineering or a related field • 7+ years of experience in a DevOps/SRE role • Strong experience with cloud computing and highly available distributed systems • Exposure to site reliability issues in critical environments (issue root cause analysis, in-production troubleshooting, on-call rotations...) • Experience working against reliability KPIs (observability, alerting, SLAs) • Hands-on experience with CI/CD, containerization and orchestration tools (Docker, Kubernetes...) • Knowledge of monitoring, logging, alerting and observability tools (Prometheus, Grafana, ELK Stack, Datadog...) • Familiarity with infrastructure-as-code tools like Terraform or CloudFormation • Proficiency in scripting languages (Python, Go, Bash...) and knowledge of software development best practices • Strong understanding of networking, security, and system administration concepts • Excellent problem-solving and communication skills • Self-motivated and able to work well in a fast-paced startup environment   Your application will be all the more interesting if you also have: • experience in an AI/ML environment • experience of high-performance computing (HPC) systems and workload managers (Slurm) • worked with modern AI-oriented solutions (Fluidstack, Coreweave, Vast...) WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

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 We are looking for a hands-on Risks Ops to own and evolve our core Decision-Making System (DMS) and credit processing engine. In this role, you will bridge the gap between risk management, and core engineering, leading a focused team of developers to design, build, and optimize high-throughput, low-latency decisioning engines. You will play a pivotal role in streamlining time-to-market for credit strategy changes, integrating advanced ML scoring models, and replacing legacy software with modern, scalable, in-house microservices architecture. Key Responsibilities Technical Leadership & Ownership: Take full ownership of the end-to-end Decision-Making System (DMS) lifecycle, including system architecture, deployment pipelines, production monitoring, continuous code cleanup, and performance optimization. Cross-Functional Collaboration: Partner closely with Risk Analysts and Data Scientists to translate complex business logic, risk rules, and ML scoring models (application, behavioral, fraud) into production-ready software. Team Leadership & Delivery: Implement core logic of DMS system to suit the needs of InDrive's lending vertical. Code reviews, release management, and performance evaluations while fostering an efficient, agile environment. Process Optimization: Reduce time-to-market for credit strategy updates by modernizing delivery workflows, introducing automated regression/retrospective testing, and eliminating dependencies on legacy release cycles. Architecture & System Integration: Drive the evolution of our high-volume microservices ecosystem (Golang, AWS). Integrate internal and external services (credit bureaus, collection platforms, fraud detection services) via REST APIs and event-driven pipelines. Skills, Knowledge and Expertise Fintech & Credit Decisioning Expertise: 7+ years of total backend engineering experience, with deep domain expertise in financial technology, credit conveyors, loan processing, debt collection systems, or automated decisioning engines. Risk-Ops experience: be familiar with any popular decision-engine (gorules, n8n, camunda, SAS RTDM, FICO Blaze Advisor etc…) Stack: Strong expertise in Golang / Python, and build tools (Gitlab CI), AWS-related experience will be a huge plus. Data Management: Proficiency with relational and non-relational databases (MySQL, AuroraDB, DynamoDB, GBQ). DevOps, CI/CD & Observability: Hands-on experience with modern CI/CD tools (GitLab CI, GitHub Actions), containerization (Docker), and system monitoring tools (Prometheus, Grafana). 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. Annual salary from 75 600 EUR gross. Final benefits may vary depending on the location.

Location: Yerevan, Armenia Workplace: hybrid About Webb Fontaine Founded in 2002, Webb Fontaine is a global trade technology company headquartered in Dubai, with projects spanning the Middle East, Africa, Asia, South America and beyond. We partner with governments worldwide, providing cutting-edge solutions to boost their revenue by streamlining trade and logistics operations across the entire trade spectrum. At Webb Fontaine, we're crafting a legacy of innovation with a mission to transform the landscape of trade through technology. This wouldn’t be possible without our team of exceptional individuals who are at the core of our success. Build the technology powering international trade. We're looking for a Mid-Level Java Software Engineer to help build a modern, scalable platform supporting mission-critical Customs operations. You'll work on real distributed systems, grow your architectural skills alongside experienced engineers, and use modern AI tools to deliver reliable, maintainable software. Our Tech Stack Java 17+, Spring Boot, Apache Kafka, PostgreSQL, Oracle, Elasticsearch, Amazon S3, Kubernetes, and AI coding assistants. Key responsibilities Build and maintain scalable, reliable backend services using Java, Spring Boot, and event-driven architectures. Apply best practices in software design, testing, code reviews, and CI/CD. Troubleshoot production issues and optimize application performance. Use AI coding assistants and agents to accelerate development, generate tests, refactor code, and debug issues. Review and validate AI-generated code for correctness, security, and maintainability. Collaborate with the team. Travel to our project offices when necessary. Qualifications 3+ years of professional Java development experience, including Spring Boot. Solid knowledge of SQL databases, Kafka, microservices, and event-driven architecture. Understanding of JVM performance, multithreading, and concurrency. Experience with automated testing, CI/CD, and containerized applications. Practical experience with AI coding assistants such as Claude Code or Codex, with the ability to critically evaluate generated code. Strong analytical and problem-solving skills. Fluency in English . Availability and willingness to travel to project offices are mandatory. Nice-to-have qualifications Experience with AWS, GCP, or Azure. Kubernetes, Elasticsearch, or observability tools (Grafana, Prometheus). Experience with Customs, trade, or government systems. Benefits Equal opportunity advocacy: We promote equality, creating a diverse and inclusive environment. Competitive compensation: Enjoy a competitive salary that rewards your contributions. Health and Wellness: We provide comprehensive medical coverage. Global collaboration: Join a world-class team with a global presence and international clients. Investment in you: We invest in your growth and development, valuing our employees as top talent. Work-life balance: We support a healthy work-life balance for your well-being. Training support: We offer a comprehensive learning and development program. We offer benefits tailored to each location, considering the specific regulations and conditions of each country. These will be discussed with candidates during the selection process.

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