MLOps Engineer in London

We're looking for an MLOps Engineer to join our team in London, in a hybrid working mode.In this role, you will build, deploy and maintain production-ready machine learning solutions with a strong focus on MLOps practices including CI/CD, model serving, monitoring and robust cloud infrastructure. You will also contribute to extending these capabilities toward LLMOps and agentic AI workflows, enabling areas such as LLM applications, RAG pipelines, model evaluation and observability for enterprise environments.The position involves close collaboration with engineering and data science teams as well as advisory engagement with clients on best practices in AI infrastructure and operational scalability. This is an opportunity to deliver impactful AI capabilities while working at the intersection of modern AI and enterprise systems.ResponsibilitiesBuild and maintain platform components for ML model training, deployment, serving and monitoringDevelop and optimize CI/CD pipelines for machine learning workflowsImplement and support model lifecycle management, including registries and observability toolingDesign and manage scalable, secure deployments using containerization and KubernetesEnable secure, reusable and automated workflows to enhance ML developer productivityExtend platform capabilities to support LLMOps, RAG and agentic AI workloadsCollaborate with engineering teams to improve reliability, automation and operational maturityApply governance and compliance standards across AI operationsParticipate in presales and client-facing sessions to translate requirements into scalable solutionsAdvocate cloud best practices for reliability, scalability and cost optimizationRequirementsBachelor’s or Master’s degree in Computer Science, Engineering or related disciplineExperience in delivering machine learning or MLOps systems into production environmentsProficiency in Python for building services, APIs, scripts and CI/CD automationWorking knowledge of modern MLOps stacks including experiment tracking and artifact managementHands-on experience with orchestration tools (e.g., Kubeflow, Apache Airflow, Metaflow or Prefect)Demonstrated skills with Docker, Kubernetes and distributed deploymentsPractical 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 productionStrong communications skills to convey technical decisions and engage with clients effectivelyNice to haveBackground deploying Generative AI solutions, LLM inference pipelines or agentic AI systemsExperience with feature stores, vector databases and retrieval-augmented generation approachesKnowledge of AI governance, security and compliance for regulated sectorsFamiliarity with advanced observability and tracing solutions, such as OpenTelemetry or LangfuseConsulting or enterprise architecture experience in large-scale AI programsUnderstanding of FinOps strategies for managing GPU/CPU costs in cloud environmentsCertifications in cloud technologies (AWS, Azure, GCP) or Kubernetes (CKA/CKAD)Expertise in securing and operationalizing ML/LLM/agent-based systems for enterprise readinessWe offerEPAM Employee Stock Purchase Plan (ESPP)Protection benefits including life assurance, income protection and critical illness coverPrivate medical insurance and dental careEmployee Assistance ProgramCompetitive group pension planCyclescheme, Techscheme and season ticket loansVarious perks such as free Wednesday lunch in-office, on-site massages and regular social eventsLearning and development opportunities including in-house training and coaching, professional certifications, and coursesIf otherwise eligible, participation in the discretionary annual bonus programIf otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program
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