Data/MLOps Engineer

🏢 Summary: The offer is for a Software Engineer (Data) role focused on building scalable data infrastructure and production-ready ML systems within an AI-driven environment. The position combines backend engineering, data engineering, and MLOps to design high-throughput pipelines and support AI solutions from experimentation to production. The role involves close collaboration with Data Science teams to shape architecture and engineering standards for next-generation data platforms. 🗂️ Requirements: 4+ years of software engineering experience, Strong proficiency in Python, Experience building scalable production systems, Experience with data-intensive applications and SQL databases, Knowledge of data modeling and query optimization, Experience with Terraform, Kubernetes, Docker or similar tools, Understanding of CI/CD pipelines and Infrastructure as Code, Experience implementing automated testing and clean architecture principles, Ability to productionize ML solutions with Data Science teams 📃 Skills: Python, MySQL, PostgreSQL, Spark, Terraform, Kubernetes, Docker, Airflow, SQL, CI/CD, AWS, Snowflake, DBT, MLOps 🏢 Description: Our client helps small teams power big businesses with the must-have platform for intelligent marketing automation. Customers from over 170 countries depend on the Client’s mix of pre-built automation and integration to power personalized marketing, transactional emails, and one-to-one CRM interactions throughout the customer lifecycle. We’re looking for a Software Engineer (Data) to join our client’s growing AI and Data organization and help build the scalable foundations behind next-generation data and ML systems. In this role, you won’t just be working with data infrastructure; you’ll be shaping the software architecture, engineering standards, and production-ready platforms that power the company’s AI ecosystem. This is more than a traditional backend or data engineering position. You’ll work at the intersection of software engineering, data, and AI, partnering closely with Data Science and AI teams to bridge the gap between experimentation and production. From designing resilient systems to building scalable MLOps pipelines, your work will directly influence how AI solutions are developed, deployed, and scaled across the organization. Key takeaways: Stack: Python, MySQL, PostgreSQL, Spark, Terraform, Kubernetes, Docker Salary : 20 000 - 23 000 PLN gross/month, Contract of employment (+10% annual bonus, 75% Creative Tax) Working model: Hybrid, once a week in the office Location: Krakow, ul. Konopnickiej Recruitment process: Call with MOTIFE recruiter (30 min) Interview with Hiring Manager (45 min) Technical interview, live coding (1h) Cross-functional interview (1h) Responsibilities: Design, develop, and maintain scalable high-throughput data pipelines across complex data ecosystems. Build and optimize data models, schemas, and database structures to ensure long-term scalability and performance. Implement engineering best practices, including automated testing, CI/CD pipelines, and Infrastructure as Code (Terraform). Partner closely with AI and Data Science teams to productionize machine learning solutions and develop scalable MLOps pipelines. Engineer and maintain feature stores and data infrastructure supporting AI-driven initiatives. Monitor, maintain, and improve the reliability and efficiency of containerized environments using Kubernetes, Docker, and Airflow. Ensure platform stability, observability, and operational excellence through proactive system monitoring and health checks. Collaborate cross-functionally with engineering and business stakeholders to translate complex technical concepts into actionable insights. Contribute to the architectural direction and scalability of the organization’s AI and data platforms. Drive the adoption of robust software engineering standards across data and infrastructure projects. Requirements: 4+ years of experience in software engineering, with strong hands-on experience in backend development and building scalable production systems. Strong proficiency in Python and solid software engineering fundamentals, including clean architecture, testing, and maintainable code practices. Experience working with data-intensive applications, databases, and SQL, including data modeling and query optimization. Exposure to modern data engineering, cloud, or infrastructure environments, with familiarity in tools such as Terraform, Kubernetes, Docker, or similar technologies. Understanding of CI/CD pipelines, Infrastructure as Code, and general engineering best practices. Interest in AI/ML ecosystems and willingness to work closely with Data Science and AI teams on productionizing ML solutions. Familiarity with cloud platforms and modern data stack technologies such as AWS, Snowflake, Spark, or DBT is considered a strong plus. Ownership mindset and comfort working in evolving, fast-moving environments where systems and processes are still being built. What we offer: Health Benefits 1. Medical Full coverage for employees and their dependents through LUX MED. Employees have access to the “Premium” package, providing enhanced coverage and greater access to care. A client pays 100% of the premium for employees and 50% for dependents. 2. Dental No additional cost for dental coverage- integrated into LUX MED medical plan. 3. Vision Reimbursement for vision expenses up to 400 PLN every 2 years. Mental Health Tools Access to TELUS Health EAP to provide support and resources in a time of need. Additional Benefits 10% annual bonus 75% Creative Tax Vacation: 26 days. Home Office Stipend: One-time $150 equivalent home office stipend to outfit their home office. Calm Subscription: Premium subscription access to Calm, the #1 app for sleep, meditation, and relaxation. Hub Perks: Receive meal and transportation benefits when traveling to the Poland Hub. Baby Swag: If you have a baby or adopt, you’ll receive a company-branded first bath bundle. Sabbatical Program: After 5 years of employment, receive a month-long paid sabbatical leave, with a sabbatical leave bonus.

🏢 Summary: Contract role for a Senior Data Engineer with strong MLOps expertise to design and maintain end-to-end data and ML pipelines in Azure Databricks. The position focuses on building reproducible training and deployment workflows, implementing data quality and monitoring frameworks, and optimizing performance and cost in production environments. The engineer will also ensure security, compliance, and infrastructure consistency using IaC and CI/CD practices. 🗂️ Requirements: Senior-level experience in Data Engineering, Strong MLOps background, Hands-on experience with Azure Databricks (Workflows, Delta Lake, Unity Catalog), Proficiency in PySpark and SQL Warehouses, Experience building production-grade data and ML pipelines, Experience with CI/CD pipelines (GitHub Actions or Azure DevOps), Experience with monitoring and ML observability tools, Knowledge of data quality frameworks and drift detection, Experience with security, access management and compliance in data/ML environments, Experience with Infrastructure as Code using Terraform 📃 Skills: Azure, Databricks, PySpark, SQL, DeltaLake, UnityCatalog, CI/CD, GitHubActions, AzureDevOps, Lakeview, Grafana, Terraform, MLOps, IaC 🏢 Description: Senior Data Engineer / MLOps Remote, Poland B2B till August 160–180 PLN/h Responsibilities Design & maintain end-to-end data & ML pipelines (Databricks Workflows, Delta Lake, Unity Catalog) Build reproducible training & deployment workflows (experiment tracking, model registry, artifact management) Implement data quality frameworks, observability metrics, dashboards (Lakeview, Grafana) Automate ingestion & feature pipelines via PySpark, SQL Warehouses, DAB within CI/CD Manage security, access control & compliance in data/ML environments Optimize compute performance & cost (autoscaling, spot instances, caching, partitioning) Develop automated evaluation & validation pipelines (new telemetry, reproducibility, traceability) Maintain continuous model & data monitoring (drift, feature stability, prediction quality) Ensure environment consistency via IaC & containerization (Terraform) Requirements Senior Data Engineer + solid MLOps background Azure Databricks (Workflows, Delta Lake, Unity Catalog) PySpark, SQL Warehouses, production-grade pipeline development CI/CD (GitHub Actions / Azure DevOps) Monitoring tools (Lakeview, Grafana), ML observability Data quality, drift detection, ML monitoring Security, access management & compliance in data/ML ecosystems Terraform / IaC

🏢 Summary: The role focuses on building and operating scalable, low-latency machine learning infrastructure for real-time personalization services. You will design and maintain Python-based microservices, deploy ML models to cloud environments, and ensure high availability and performance of recommendation systems. The position involves close collaboration with ML and platform teams to bring ML-powered features into production. 🗂️ Requirements: 3+ years of professional software engineering experience, Degree in Computer Science, Engineering, or related technical field, Strong knowledge of data structures and algorithms, Professional experience building backend services in Python, Experience designing RESTful APIs or gRPC services, Experience building microservices architectures, Experience deploying production services on AWS, GCP, or Azure, Experience with relational and non-relational databases, Experience with event-driven architectures and message queues, Strong debugging, profiling, and performance tuning skills 📃 Skills: Python, AWS, GCP, Azure, Kubernetes, Docker, REST, gRPC, Kafka, RabbitMQ, SQS, Postgres, MySQL, DynamoDB, Redis, CI/CD, Terraform, Datadog, Grafana, MLflow 🏢 Description: We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere. We are looking for a Machine Learning Infrastructure Engineer to join the Personalization team, which owns the systems powering content recommendations across the company’s digital ecosystem. In this role, you will design, build, and maintain low-latency, highly scalable services that make real-time personalization possible. You will work hands-on with backend services, cloud infrastructure, model serving, observability, and performance optimization, partnering closely with ML Engineers, API Engineers, Platform Engineers, and Product Managers to bring ML-powered product features into production. Key takeaways: Stack : Python, AWS/GCP/Azure, Kubernetes, Docker, REST, gRPC, Kafka/RabbitMQ/SQS, Postgres, MySQL, DynamoDB, Redis, CI/CD, Terraform, Datadog/Grafana/MLflow Salary : 24 000 PLN - 28 000 PLN gross on the Contract of Employment Working model : Hybrid - 3 days/week from the office Location : ul. Grzybowska 60, Warsaw Recruitment process : A call with MOTIFE Recruiter Hiring Manager screening Coding interview Panel interviews with the team (coding, architecture, and cross-collaboration interviews, Hiring Manager meeting) Responsibilities: Design, build, and maintain Python microservices powering personalized content recommendations. Productionize, deploy, monitor, and operate machine learning services in cloud-based production environments. Partner with ML Engineers to integrate models into scalable backend services and real-time recommendation workflows. Ensure high availability, low latency, and strong performance through caching, load balancing, auto-scaling, and capacity planning. Own and improve personalization services, including reliability, testability, observability, scalability, and operational readiness. Conduct performance tuning, profiling, and latency optimization for high-traffic recommendation workloads. Collaborate with platform teams to use infrastructure, tooling, and deployment workflows that support fast product iteration. Work with Product Managers, ML Engineers, API Engineers, and Data Engineers to launch ML-powered personalization features. Requirements: 3+ years of professional software engineering experience and a degree in Computer Science, Engineering, or a related technical field. Strong software engineering fundamentals, including data structures, algorithms, clean code, testing, and reproducibility. Professional experience building backend services in Python; experience with Java, Kotlin, Go, C, or C++ is welcome. Experience designing and building RESTful APIs, gRPC services, or microservices from the ground up. Strong experience deploying and managing production services on AWS or GCP, or Azure. Experience with relational and non-relational databases such as Postgres, MySQL, DynamoDB, or Redis. Experience with event-driven architectures and message queues such as Kafka, RabbitMQ, or SQS. Strong debugging, profiling, and performance tuning skills, including latency tracking, scalability analysis, and production troubleshooting. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If this sounds like your next step, we’d love to hear from you! Please apply via our careers page and submit your CV in English.

🏢 Summary: The role involves building and operating scalable, low-latency machine learning infrastructure to power real-time personalization and content recommendations. You will design and maintain Python-based microservices, deploy ML models to cloud environments, and ensure high availability and performance of production systems. The position focuses on backend engineering, cloud infrastructure, and model serving within a cross-functional ML product team. 🗂️ Requirements: 3+ years of professional software engineering experience, Degree in Computer Science, Engineering, or related technical field, Strong knowledge of data structures and algorithms, Professional experience building backend services in Python, Experience designing RESTful APIs or gRPC services, Experience building microservices architectures, Experience deploying production services on AWS, GCP, or Azure, Experience with relational and non-relational databases, Experience with event-driven architectures and message queues, Strong debugging, profiling, and performance tuning skills 📃 Skills: Python, AWS, GCP, Azure, Kubernetes, Docker, REST, gRPC, Kafka, RabbitMQ, SQS, Postgres, MySQL, DynamoDB, Redis, CI/CD, Terraform, Datadog, Grafana, MLflow 🏢 Description: We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere. We are looking for a Machine Learning Infrastructure Engineer to join the Personalization team, which owns the systems powering content recommendations across the company’s digital ecosystem. In this role, you will design, build, and maintain low-latency, highly scalable services that make real-time personalization possible. You will work hands-on with backend services, cloud infrastructure, model serving, observability, and performance optimization, partnering closely with ML Engineers, API Engineers, Platform Engineers, and Product Managers to bring ML-powered product features into production. Key takeaways: Stack : Python, AWS/GCP/Azure, Kubernetes, Docker, REST, gRPC, Kafka/RabbitMQ/SQS, Postgres, MySQL, DynamoDB, Redis, CI/CD, Terraform, Datadog/Grafana/MLflow Salary : 24 000 PLN - 28 000 PLN gross on the Contract of Employment Working model : Hybrid - 3 days/week from the office Location : ul. Grzybowska 60, Warsaw Recruitment process : A call with MOTIFE Recruiter Hiring Manager screening Coding interview Panel interviews with the team (coding, architecture, and cross-collaboration interviews, Hiring Manager meeting) Responsibilities: Design, build, and maintain Python microservices powering personalized content recommendations. Productionize, deploy, monitor, and operate machine learning services in cloud-based production environments. Partner with ML Engineers to integrate models into scalable backend services and real-time recommendation workflows. Ensure high availability, low latency, and strong performance through caching, load balancing, auto-scaling, and capacity planning. Own and improve personalization services, including reliability, testability, observability, scalability, and operational readiness. Conduct performance tuning, profiling, and latency optimization for high-traffic recommendation workloads. Collaborate with platform teams to use infrastructure, tooling, and deployment workflows that support fast product iteration. Work with Product Managers, ML Engineers, API Engineers, and Data Engineers to launch ML-powered personalization features. Requirements: 3+ years of professional software engineering experience and a degree in Computer Science, Engineering, or a related technical field. Strong software engineering fundamentals, including data structures, algorithms, clean code, testing, and reproducibility. Professional experience building backend services in Python; experience with Java, Kotlin, Go, C, or C++ is welcome. Experience designing and building RESTful APIs, gRPC services, or microservices from the ground up. Strong experience deploying and managing production services on AWS or GCP, or Azure. Experience with relational and non-relational databases such as Postgres, MySQL, DynamoDB, or Redis. Experience with event-driven architectures and message queues such as Kafka, RabbitMQ, or SQS. Strong debugging, profiling, and performance tuning skills, including latency tracking, scalability analysis, and production troubleshooting. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If this sounds like your next step, we’d love to hear from you! Please apply via our careers page and submit your CV in English.

🏢 Summary: Senior Data Engineer role focused on designing and building scalable serverless data ingestion pipelines in AWS within the healthcare domain. The position emphasizes strong Python engineering, cloud architecture leadership, and implementation of modern data platforms and DevOps practices. The role involves driving technical excellence and delivering reliable, high-impact data solutions in an international environment. 🗂️ Requirements: 10+ years of experience in Python programming, Strong software engineering skills in data processing, Extensive experience with AWS Cloud and Serverless Architecture, Hands-on experience with AWS Lambda, S3, and Cognito, Experience building E2E automated tests for data pipelines, Practical knowledge of Data Mesh and Medallion Architecture, Experience with Infrastructure as Code using AWS CDK or Terraform, Experience with CI/CD pipelines using GitLab or GitHub Actions, Experience with ETL/ELT processes and dbt, Experience working with GraphQL, Minimum B2 level English proficiency 📃 Skills: Python, AWS, Lambda, S3, Cognito, Boto3, DataMesh, Medallion, CDK, Terraform, GitLab, GitHubActions, ETL, ELT, dbt, GraphQL, CI/CD 🏢 Description: We are looking for a highly skilled Data Engineer to join our client in the healthcare sector. Our requirements: Technical Expertise: Python Programming: 10+ years of experience with strong Software Engineering skills focused on data processing. AWS & Serverless: Extensive experience with AWS Cloud, specifically focusing on Serverless Architecture and services (including AWS Lambda , AWS S3 Tables , and AWS Cognito ). Automated Testing: Proven experience in developing End-to-End (E2E) automated tests to ensure pipeline reliability, utilizing tools such as Boto3 for AWS resource validation. Data Concepts: Practical knowledge of Data Mesh and Medallion Architecture , along with general data processing and analysis. DevOps & IaC: Hands-on experience with Infrastructure as Code ( AWS CDK or Terraform ) and CI/CD pipelines ( GitLab pipelines or GitHub Actions ). Modern Tooling: Experience with ETL/ELT solutions, dbt , and GraphQL . Communication & Soft Skills: English Language: Minimum B2 level , enabling smooth daily technical and business communication in a global environment. Collaboration: Excellent communication skills and the ability to thrive in a collaborative, international team. Standards: A strong commitment to high standards of ethics, quality (Clean Code), and reliable delivery. Nice to have: Experience with Snowflake and SQL . Knowledge of Data Vault 2.0 modeling. Experience with Databricks . Familiarity with the Microsoft ecosystem: C# / .Net, T-SQL, SQL Server , and Azure DevOps . Experience with Star Schema database modeling. Knowledge of Descriptive Statistics. Your responsibilites: Design and build scalable Data Ingestion pipelines within the AWS cloud ecosystem. Lead technical delivery and implementation of core platform components, ensuring architectural integrity across the entire data lifecycle. Collaborate with Engineering Managers and cross-functional teams across the globe and Poland. Drive technical excellence by improving team processes, architecture standards, and engineering best practices. Support and consult with stakeholders to ensure successful delivery of high-impact, data-driven solutions. Contribute to the growth and maturity of the team’s cloud and data engineering capabilities. We offer: Challenging role within the company that creates innovative solutions. Work in international environment on demanding projects. Remote work model. Subsidized private medical care, life insurance, multisport card. Integration meetings. Employee referral program. If you have a deep expertise in Python and AWS , and building scalable Serverless data architectures is where you truly excel, this is the perfect role for you!

🏢 Summary: The offer is for a DevOps Engineer responsible for building and supporting tools, processes, and infrastructure that enable fast, reliable, and scalable software delivery. The role focuses on CI/CD, cloud infrastructure, container orchestration, observability, and integrating AI-powered tools into the software development lifecycle. It involves ensuring high availability, automation, and operational insight across AWS-based environments. 🗂️ Requirements: 3+ years of experience with DevOps practices and Continuous Delivery, Practical experience with AI-powered development assistants, Hands-on experience with AWS services and networking, Experience with Kubernetes and container orchestration, Knowledge of Python, Experience with GitOps practices, Experience with CI/CD pipelines and tooling, Experience with monitoring, logging, and observability tools, Knowledge of Terraform and Helm, Proficiency in Bash and PowerShell scripting, Knowledge of API management tools, Security awareness (OWASP, encryption, secrets management), Upper intermediate/advanced English (B2/C1), Work permit and residence in the EU 📃 Skills: AWS, Kubernetes, EKS, Python, GitOps, ArgoCD, Terraform, Helm, Bash, PowerShell, GitLab, GitHub, CI/CD, Prometheus, Grafana, AppDynamics, OpenSearch, Apigee, Tyk, Kiro, OWASP, Encryption 🏢 Description: 🟣 You will be: building and supporting the tools, processes and infrastructure empowering the faster delivery and scaling of software iterations (ideally with greater quality and the necessary operational insight), ensuring availability, reliability and scalability of application infrastructure, building and supporting continuous integration/delivery and release tools, ensuring the right metrics are collected and monitored. 🟣 Your profile: mart and tech-savvy engineer with 3+ years of experience working with DevOps practices and Continuous Delivery, practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery, practical knowledge of AWS services, infrastructure and networking, working experience with Kiro, practical knowledge of Tyk, proficiency in Google Cloud Apigee, experience with Kubernetes (ideally EKS on AWS) and container orchestration, Python knowledge, hands-on with GitOps practices, preferably with ArgoCD, medium skills in Terraform and Helm, proficiency in Bash and PowerShell scripting, experience with CI/CD pipelines and tooling (GitLab CI/CD, GitHub Actions, or similar), experience with monitoring, observability, and logging tools, such as Prometheus, Grafana, AppDynamics, and OpenSearch, security awareness (OWASP, encryption, secrets management), very communicative and collaborative, with a strong sense of ownership, upper intermediate/advanced English (B2/C1). Work from the European Union region and a work permit are required. 🟣 Nice to have: Experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work. Interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches. 🟣 Recruitment Process: CV review – HR Interview – Technical Interview - Client Interview – Decision 🎁 Benefits 🎁 ✍ Development: development budgets of up to 6,800 PLN, we fund certifications e.g.: AWS, Azure, access to Udemy, O'Reilly (formerly Safari Books Online) and more, events and technology conferences, technology Guilds, internal training, Xebia Upskill. 🩺 We take care of your health: private medical healthcare, MultiSport card - we subsidise a MultiSport card, mental Health Support.

🏢 Summary: The role involves owning the full MLOps lifecycle for an AI-powered sports highlights platform, from model research and training to production deployment and scalable cloud infrastructure. You will build and maintain ML pipelines, automate workflows across Computer Vision and NLP, and ensure reliable CI/CD processes in a high-uptime environment. 🗂️ Requirements: 4+ years experience in MLOps, DevOps, ML Engineering or similar role, Experience in large-scale, high-uptime production cloud environments, Strong knowledge of Linux, Experience with Docker, Experience with Kubernetes, Experience building CI/CD pipelines for distributed systems, Proficiency in Python scripting, Experience with Terraform, Understanding of machine learning fundamentals and frameworks 📃 Skills: Python, Linux, Docker, Kubernetes, Terraform, CI/CD, ClearML, W&B, SageMaker, Azure, ComputerVision, NLP, MLOps 🏢 Description: We’re seeking a skilled and motivated MLOps Engineer to join a fast-moving team building an AI-powered platform for automatic sports highlights generation. In this role, you’ll take ownership of the full MLOps lifecycle—from research and model training to production deployment and scalable infrastructure. You’ll design and maintain robust ML pipelines, automate workflows across Computer Vision, NLP, and Data Science, and build reliable CI/CD processes that keep high-uptime systems running smoothly. Your work will help deliver cutting-edge AI experiences at cloud scale, powering innovation and performance across the entire platform. Responsibilities Own and manage every aspect in the MLOps life cycle for our AI-based automatic sports highlights generation platform Design, implement and maintain our whole ML infrastructure, from research to production and from model training to data engineering Automate and innovate workflows such as serving and training pipelines for multidisciplinary ML algorithms including Computer Vision, NLP and Data Science Build and maintain CI/CD pipelines, releases and Source Code workflows Requirements 4+ years of experience as an MLOps engineer, DevOps engineer, ML Engineer, or in a similar field Experience in a large, complex, large-scale, high-uptime production Cloud environment Core understanding of Linux OS, Docker components, and Kubernetes Experience with CI/CD pipelines for distributed production systems Experience with Python scripting Work experience with Terraform Working with MLOps platforms such as Experiment Tracking, Model Registry, feature Store - an advantage (e.g ClearML , W&B, Aws Sagemaker) Highly motivated, goal-driven, innovative, curious, and open-minded Will be a plus Working with Azure Cloud in a high-scale production environment Understanding of AI and machine learning fundamentals, concepts and frameworks What we offer Competitive salary range Medical insurance Paid vacation and sick leaves MultiSport card Top equipment kit, co-workings Hybrid set of works (Office location: Warsaw) Collaborative and innovative work environment Career growth and development opportunities A chance to work with giants of the sports industry About the project Our partner leads the industry in generating dynamic sports videos for every digital destination. Their cutting-edge AI and Machine Learning technologies analyse live sports broadcasts from over 250 leagues and broadcast partners, including iconic names like the NBA, NHL, ESPN, FIBA and Bundesliga, to create personalized, short-form videos in real-time. The solution empowers media rights owners to unlock new revenue streams and deliver a tailored fan experience across every digital platform. Join the high-profile Engineering team and discover the forefront of sports content innovation.

🏢 Summary: The offer is for a senior DevOps/Platform Engineer role focused on building and scaling CI/CD pipelines, cloud infrastructure, and observability solutions to ensure high availability, reliability, and fast software delivery. The position involves working with Kubernetes, cloud platforms, GitOps, and AI-related tooling to support modern, scalable application environments. It requires strong hands-on technical leadership and expertise in automation, infrastructure as code, and monitoring. 🗂️ Requirements: 5+ years of experience with DevOps practices and Continuous Delivery, Hands-on experience with AWS and Azure services, Strong experience with Kubernetes (EKS, AKS) and container orchestration, Proficiency in Terraform and Helm, Experience with GitOps practices (e.g., ArgoCD), Experience with CI/CD pipelines (GitLab CI/CD, GitHub Actions or similar), Strong scripting skills in Bash and PowerShell, Python knowledge, Experience with monitoring and observability tools, Strong networking knowledge, Experience with AI Agents and Claude Code, Experience with FastMCP or other MCP libraries, Knowledge of security best practices (OWASP, encryption, secrets management), Upper intermediate/advanced English (B2/C1), Work permit and residence in the European Union 📃 Skills: AWS, Azure, Kubernetes, EKS, AKS, Terraform, Helm, ArgoCD, GitLab, GitHub, Python, Bash, PowerShell, Prometheus, Grafana, AppDynamics, OpenSearch, Claude, FastMCP, FluxCD, Harness, Rancher, Keycloak, Networking, GitOps, CI/CD 🏢 Description: 🟣 You will be: building and supporting the tools, processes and infrastructure empowering the faster delivery and scaling of software iterations (ideally with greater quality and the necessary operational insight), ensuring availability, reliability and scalability of application infrastructure, building and supporting continuous integration/delivery and release tools, ensuring the right metrics are collected and monitored. 🟣 Your profile: hands-on experience as a tech leader, smart and tech-savvy engineer with 5+ years of experience working with DevOps practices and Continuous Delivery, practical knowledge of AWS and Azure services, infrastructure and networking, solid experience with Kubernetes (EKS and AKS) and container orchestration, Python knowledge, strong networking skills, experience in Claude Code, experience in working with AI Agents, experience in FastMCP or other MCP libraries, hands-on with GitOps practices, preferably with ArgoCD, strong skills in Terraform and Helm, proficiency in Bash and PowerShell scripting, experience with CI/CD pipelines and tooling (GitLab CI/CD, GitHub Actions, or similar), experience with monitoring, observability, and logging tools, such as Prometheus, Grafana, AppDynamics, and OpenSearch, security awareness (OWASP, encryption, secrets management), very communicative and collaborative, with a strong sense of ownership, upper intermediate/advanced English (B2/C1). Work from the European Union region and a work permit are required 🟣 Nice to have: AWS/ Azure certifications such as Solutions Architect or Platform Engineer, familiarity with FluxCD, experience with Harness, experience with Rancher, knowledge of Keycloak. 🟣 Recruitment Process: CV review – HR Interview – Technical Interview - Client Interview – Decision 🎁 Benefits 🎁 ✍ Development: development budgets of up to 6,800 PLN, we fund certifications e.g.: AWS, Azure, access to Udemy, O'Reilly (formerly Safari Books Online) and more, events and technology conferences, technology Guilds, internal training, Xebia Upskill. 🩺 We take care of your health: private medical healthcare, multiSport card - we subsidise a MultiSport card, mental Health Support. 🤸‍♂️ We are flexible: B2B or employment contract, contract for an indefinite period.

🏢 Summary: Long-term B2B contract for an MLOps/LLMOps Engineer to design and build a scalable AI/LLM platform in Azure and Kubernetes environments. The role focuses on automating ML lifecycle processes, managing production-grade ML systems, and ensuring observability and compliance with AI regulations. Hybrid work model with at least one day per week onsite in Warsaw. 🗂️ Requirements: Minimum 3 years of commercial experience in DevOps, MLOps or ML production engineering, Strong knowledge of Kubernetes and Docker including cluster management and Helm charts, Experience with public cloud Azure or GCP or AWS, Experience in Python scripting, Experience in Bash or Shell scripting, Experience with ML lifecycle management tools such as MLflow or Kubeflow, Experience with Infrastructure as Code tools such as Terraform or Bicep or Ansible, Ability to provide services from Poland 📃 Skills: Azure, AzureML, AKS, Kubernetes, Docker, Helm, Python, Bash, MLflow, Kubeflow, Terraform, Bicep, Ansible, GCP, AWS, DVC, CICD 🏢 Description: MLOps / LLMOps Engineer (Azure & Kubernetes) Dla naszego klienta – lidera sektora ubezpieczeń, który rewolucjonizuje swoje usługi za pomocą AI – szukamy inżyniera gotowego wziąć na klatę budowę nowoczesnej platformy LLMOps pod najnowsze wymogi AI Act. Jeśli automatyzację stawiasz na pierwszym miejscu, a środowiska produkcyjne z modelami ML to Twój chleb powszedni – czytaj dalej! ⚡ Kluczowe informacje: Forma współpracy: Kontrakt B2B, Długofalowy Model pracy: Hybryda (Warszawa, Wola, blisko komunikacji miejskiej – min. 1 dzień w tygodniu w biurze) 🛠️ Twój codzienny stack & wyzwania: Infrastruktura AI/LLM: Projektowanie i budowa skalowalnych środowisk w oparciu o Azure Machine Learning, Azure AI Foundry oraz Kubernetes (AKS). Automatyzacja (CI/CD/CT): Wdrażanie potoków dla ML, wersjonowanie danych i modeli (DVC, MLflow) oraz Continuous Training. Observability: Monitoring modeli pod kątem Data Drift/Model Drift na produkcji. Architektura: Konteneryzacja (Docker), zarządzanie klastrami (Helm) oraz praca na styku systemów chmurowych i on-premise. 🎯 Czego oczekujemy? (Must Have): Min. 3 lata komercyjnego doświadczenia w obszarze DevOps, MLOps lub inżynierii oprogramowania z modelami ML na produkcji Bardzo dobrej znajomości Kubernetes & Docker (zarządzanie klastrami, Helm charts). Doświadczenia z chmurą publiczną (głęboki Azure lub GCP/AWS, jeśli chcesz szybko wejść w Azure). Praktyki w pisaniu skryptów w Python oraz Bash/Shell. Znajomości narzędzi do zarządzania cyklem życia modeli (np. MLflow , Kubeflow) oraz podejścia Infrastructure as Code (Terraform, Bicep lub Ansible). Świadczenia usług z terytorium Polski. ⭐ Mile widziane (Nice to Have): Doświadczenie we wdrażaniu modeli LLM i architektur RAG . Znajomość baz wektorowych (np. Azure AI Search) oraz narzędzi do monitoringu (Prometheus, Grafana). Certyfikaty: Azure DevOps Engineer Expert (AZ-400) lub Azure AI Engineer (AI-102). Oferujemy: Preferencyjne pakiety do wykupienia na Multisport i Luxmed. Współpracę w ramach kontraktu B2B. 👋 Brzmi jak Twój kolejny krok zawodowy? Aplikuj!

🏢 Summary: B2B role for an MLOps/LLMOps Engineer to design and build a scalable LLM platform on Azure and Kubernetes, ensuring automated CI/CD/CT pipelines and production-grade ML model operations. The position focuses on AI infrastructure, model lifecycle management, observability, and hybrid cloud/on-prem integration. Hybrid work model with long-term cooperation. 🗂️ Requirements: Minimum 3 years of commercial experience in DevOps, MLOps or ML engineering in production environments, Strong hands-on experience with Kubernetes and Docker, including cluster management and Helm, Experience with public cloud (Azure preferred, or GCP/AWS with readiness to work on Azure), Proficiency in Python and Bash/Shell scripting, Experience with ML lifecycle tools such as MLflow or Kubeflow, Experience with Infrastructure as Code tools (Terraform, Bicep or Ansible), Ability to provide services from Poland 📃 Skills: Azure, AzureML, AKS, Kubernetes, Docker, Helm, Python, Bash, MLflow, Kubeflow, Terraform, Bicep, Ansible, DVC, Prometheus, Grafana, GCP, AWS 🏢 Description: MLOps / LLMOps Engineer (Azure & Kubernetes) Dla naszego klienta – lidera sektora ubezpieczeń, który rewolucjonizuje swoje usługi za pomocą AI – szukamy inżyniera gotowego wziąć na klatę budowę nowoczesnej platformy LLMOps pod najnowsze wymogi AI Act. Jeśli automatyzację stawiasz na pierwszym miejscu, a środowiska produkcyjne z modelami ML to Twój chleb powszedni – czytaj dalej! ⚡ Kluczowe informacje: Forma współpracy: Kontrakt B2B, Długofalowy Model pracy: Hybryda (Warszawa, Wola, blisko komunikacji miejskiej – min. 1 dzień w tygodniu w biurze) 🛠️ Twój codzienny stack & wyzwania: Infrastruktura AI/LLM: Projektowanie i budowa skalowalnych środowisk w oparciu o Azure Machine Learning, Azure AI Foundry oraz Kubernetes (AKS). Automatyzacja (CI/CD/CT): Wdrażanie potoków dla ML, wersjonowanie danych i modeli (DVC, MLflow) oraz Continuous Training. Observability: Monitoring modeli pod kątem Data Drift/Model Drift na produkcji. Architektura: Konteneryzacja (Docker), zarządzanie klastrami (Helm) oraz praca na styku systemów chmurowych i on-premise. 🎯 Czego oczekujemy? (Must Have): Min. 3 lata komercyjnego doświadczenia w obszarze DevOps, MLOps lub inżynierii oprogramowania z modelami ML na produkcji Bardzo dobrej znajomości Kubernetes & Docker (zarządzanie klastrami, Helm charts). Doświadczenia z chmurą publiczną (głęboki Azure lub GCP/AWS, jeśli chcesz szybko wejść w Azure). Praktyki w pisaniu skryptów w Python oraz Bash/Shell. Znajomości narzędzi do zarządzania cyklem życia modeli (np. MLflow , Kubeflow) oraz podejścia Infrastructure as Code (Terraform, Bicep lub Ansible). Świadczenia usług z terytorium Polski. ⭐ Mile widziane (Nice to Have): Doświadczenie we wdrażaniu modeli LLM i architektur RAG . Znajomość baz wektorowych (np. Azure AI Search) oraz narzędzi do monitoringu (Prometheus, Grafana). Certyfikaty: Azure DevOps Engineer Expert (AZ-400) lub Azure AI Engineer (AI-102). Oferujemy: Preferencyjne pakiety do wykupienia na Multisport i Luxmed. Współpracę w ramach kontraktu B2B. 👋 Brzmi jak Twój kolejny krok zawodowy? Aplikuj!