MLOps Engineer

In this role you help build and operate production-grade AI solutions at EPAM, focusing on MLOps, CI/CD, deployment, and observability. You will extend capabilities toward LLMOps and agentic AI workflows, collaborating with engineering and data science teams and engaging clients to scale AI infrastructure. You’ll work at the intersection of modern AI and enterprise systems to deliver reliable, scalable ML platforms. This role offers exposure to cutting-edge AI workloads in a professional, client-facing environment.

• ESPP
• life assurance
• private medical insurance
• pension plan
• Cycescheme and season ticket loans
• learning and development opportunities

• Build and maintain platform components for ML model training, deployment, serving and monitoring
• Develop and optimize CI/CD pipelines for ML workflows
• Implement and support model lifecycle management including registries and observability tooling
• Design and manage scalable, secure deployments using containerization and Kubernetes
• Enable secure, reusable and automated ML developer workflows
• Extend platform capabilities to support LLMOps, RAG and agentic AI workloads
• Collaborate with engineering teams to improve reliability, automation and operational maturity
• Apply governance and compliance standards across AI operations
• Participate in presales and client-facing sessions to translate requirements into scalable solutions
• Advocate cloud best practices for reliability, scalability and cost optimization

• Bachelor’s or Master’s degree in Computer Science, Engineering or related discipline
• Experience in delivering machine learning or MLOps systems into production environments
• Proficiency in Python for building services, APIs, scripts and CI/CD automation
• Working knowledge of modern MLOps stacks including experiment tracking and artifact management
• Hands-on experience with orchestration tools (e.g., Kubeflow, Apache Airflow, Metaflow or Prefect)
• Demonstrated skills with Docker, Kubernetes and distributed deployments
• Practical knowledge of Infrastructure-as-Code (Terraform) and a major cloud provider (AWS, Azure or GCP)
• Familiarity with ML model serving, scaling and monitoring frameworks in production
• Strong communications skills to convey technical decisions and engage with clients effectively

• communication
• collaboration
• client-facing engagement
• Python
• CI/CD for ML
• MLOps stacks (experiment tracking, artifact management)