Вакансии

— Linux (RPM-based): массовая установка на железо, автоматизация, безопасность, сеть, траблшутинг. — OpenStack (nova, cinder, glance, ovn): инсталляция, HA, тюнинг, безопасность.

- Построить надежный процесс подготовки тестовых окружений. - разработать Infrastructure as Code для тестовых окружений;

We are looking for an engineer who is ready to take responsibility for a complex production environment and influence future architectural decisions. I'm looking for a skilled Middle+/Senior DevOps Engineer in our team 🚀.

Поддерживать и развивать инфраструктуру дата‑платформы: HDFS, Spark, ClickHouse, Airflow и др. Запускать и эксплуатировать Ceph‑кластер с высокими требованиями к SLA.

Задачи: — Разрабатывать и запускать автотесты — Составлять тест планы, наборы, кейсы — Работать в кросс-функциональной команде — Вести тестовую документацию Требования: — Знание Java/Kotlin, UI и API тестирования — Опыт создания фреймворков для автоматизированных тестов — Знание процессов, методологий и практик тестирования — Опыт с CI/CD (Jenkins, Gitlab) — Обязательно опыт: Java, Spring, Hibernate, Kafka, Postgres — Будет преимуществом опыт: Debezium, ArtemisMQ, Oracle, Prometheus, ELK, Grafana, опыт выстраивания CICD и gitflow Условия: — Конкурентная зарплата — Интересные и разнообразные проекты — Возможность карьерного и профессионального роста — Работа в энергичной молодой команде, готовой обмениваться опытом — Возможны секции с Live-coddingом

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: 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: New York, New York, United States About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . Role: Enterprise AI - Platform Engineer Location: New York City, Madison Square Park area Reports to: Head of Engineering Experience Level: 6+ years Work Model: Hybrid, with regular in-office presence Why Join Enterprise AI? Enterprise AI is building the platform that enables reliable deployment of AI systems across enterprise environments. As a Platform Engineer, you will build and operate the core of Turing’s Enterprise AI platform, including platform services, the model gateway, multi-tenant infrastructure, CI/CD, infrastructure as code, and observability. The central challenge is creating repeatable deployments across environments Turing may not control, including client cloud accounts, dedicated instances, and sovereign installations. As a Platform Engineer, You Will Build Core Platform Services Design, build, and operate the services that support the Enterprise AI platform, including platform infrastructure and the model gateway. Enable Enterprise Deployment Create reliable deployment systems for client cloud accounts, dedicated instances, and sovereign environments. Own Infrastructure and Delivery Systems Build and maintain CI/CD, infrastructure as code, and deployment tooling across environments using Kubernetes, Helm, Terraform or Pulumi, and GitOps practices. Build for Reliability Design systems that support secure multi-tenant operation, reliable service ownership, and clear operational accountability. Establish Observability Build observability across the platform using service-level objectives, structured logging, metrics, tracing, and alerting. Build FinOps Visibility Develop telemetry that tracks model and compute costs by tenant. The Anti-Sell Hands-on software engineering: This is not a cloud-operations or ticket-queue DevOps role. You must be able to write production software and contribute deeply to platform services. Operational ownership: You will participate in on-call operations and incident response and help build the processes required to operate a production platform. Deployment complexity: The platform must operate across client cloud accounts, dedicated instances, and sovereign environments. Team accountability: The platform must be observable, operable, and maintainable by the team rather than dependent on one individual. Our Enterprise AI Platform Engineers Typically Have Production platform engineering experience: Six or more years of platform or infrastructure engineering experience supporting production SaaS. Customer-controlled deployment experience: Experience shipping a product that customers run in their own environment, including in a VPC, a self-hosted environment, or an air-gapped environment. Strong software engineering depth: Deep expertise in at least one backend language, such as Go, Ruby, Python, or TypeScript, with the ability to read and work across the others. Cloud and infrastructure expertise: Deep experience in at least one cloud and comfort working in a second, including private networking, KMS, IAM, Kubernetes, Helm, Terraform or Pulumi, and GitOps. Reliability and observability ownership: Experience carrying a pager, running incidents, and working with tools such as OpenTelemetry, Prometheus, or Datadog. Data and platform systems experience: Familiarity with Postgres, object storage, queues, and vector stores. Candidates whose experience is limited to cloud operations or ticket-queue DevOps without software engineering, or who have never operated multi-tenant systems, will not be a fit for this role. Top-Tier Candidates Will Have Experience with model serving and model gateways, including vLLM, LiteLLM, or Triton. Experience implementing SOC 2 controls in CI. Experience deploying and operating AI systems in regulated or highly controlled environments. Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

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: 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: Poland; Warszawa, Польща On behalf of CloudTech Client #1 , SD Solutions is looking for a talented Senior DevOps Engineer - MSP to design, build, deploy, and maintain complex cloud environments for our customers using innovative automation tools and cutting-edge technologies. As a Senior DevOps Engineer, you will be responsible for architecting, maintaining, and evolving AWS cloud infrastructures, implementing and optimizing monitoring and alerting, troubleshooting issues, and developing automation to improve efficiency and reliability across environments. At AllCloud, our DevOps Engineers work with the latest and most advanced technologies. Some of the stacks we master daily for our customers include AWS, Docker, Kubernetes, Jenkins, CI/CD, Terraform, Ansible, Lambda, Python, Go, AI/ML, Big Data, IoT, and more - all while occasionally enjoying a game of 8-Ball or Nintendo Switch to keep things fun and collaborative. SD Solutions is a staffing company operating globally. Contact us to get more details about the benefits we offer. Responsibilities: Design and implement tailor-made cloud architectures based on customer needs. Manage multiple customer environments while adhering to industry best practices. Act as a trusted technical advisor to customers, ensuring alignment with business goals. Deploy advanced cloud services and automation solutions. Implement and maintain infrastructure automation and configuration management tools. Maintain a continuous feedback loop with customers to ensure deliveries meet expectations. Debug, troubleshoot, and resolve complex technical issues. Design, set up, and maintain large-scale monitoring and logging solutions. Requirements: 3+ years of hands-on experience with cloud providers (e.g., AWS, GCP). 3+ years of professional IT experience, with a strong Linux and networking background. Proven experience in a customer-facing role. Experience with security products (e.g., Firewalls, Web Gateways, Proxies). Strong experience with container technologies (Kubernetes, EKS, ECS, Swarm, Docker, etc.). Familiarity with Agile methodology. Proficiency in scripting with at least two languages: Python, Java, Bash, or PowerShell. Demonstrated creative troubleshooting and problem-solving skills. Fast learner with the ability to adapt to new technologies and domains. Strong foundations in Git and GitFlow methodologies. Experience with CI/CD platforms (Jenkins, CircleCI, CodePipeline, etc.). Hands-on experience with Infrastructure as Code (IaC) tools (Terraform, CloudFormation, CDK, Serverless Framework). Knowledge of serverless architectures . Advantages: Experience with system monitoring platforms (Datadog, Prometheus). Experience with centralized logging platforms (ELK / OpenSearch). Familiarity with GitOps methodologies (ArgoCD, Flux). Strong information security orientation. AWS certifications (Associate, Professional, or Specialty). Experience managing production-grade environments. Knowledge of AWS security tools and best practices (Security Hub, AWS Config, WAF, Shield, etc.). Familiarity with AWS Landing Zone and multi-account strategy. CKA / CKAD certifications. Eligible for or holding a security clearance. About the company: A global professional services company specializing in cloud enablement and digital transformation. An accredited partner at the highest tier with leading cloud, CRM, and data platform providers, the company helps organizations connect front and back offices through cloud adoption, data strategy, and analytics — delivering end-to-end managed services and consulting. By applying for this position, you agree to the terms outlined in our Privacy Policy. Please take a moment to review our Privacy Policy https://sd-solutions.breezy.hr/privacy-notice , and make sure you understand its contents. If you have any questions or concerns regarding our Privacy Policy, please feel free to contact us.

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: 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