MLOps Engineer

EPAM Systems; Rijswijk; Hybrid; EUR 80,000 - 110,000; Full time

Benefits offered by this job

Holiday entitlement 26 days

Pension plan scheme

Disability insurance (WGA)

Long-term disability insurance (WIA)

Employee Stock Purchase Plan (ESPP)

Commuting cost reimbursement

Laptop + corporate SIM + device

Bike lease

Employee Assistance Program

Learning & development programs

Job summary

EPAM Systems Inc seeks an MLOps Engineer to join our Amsterdam or Rijswijk team in a hybrid mode. You will build, deploy, and maintain production-ready ML solutions with emphasis on MLOps practices including CI/CD, model serving, monitoring, and scalable cloud infrastructure.

You will collaborate with engineering and data science teams and engage clients on AI infrastructure best practices, governance and operational maturity.

Qualifications

• Experience delivering ML/MLOps systems into production.
• Proficiency in Python for building services, APIs, scripts and CI/CD automation.
• Working knowledge of modern MLOps stacks including experiment tracking and artifact management.

Responsibilities

• Build and maintain platform components for ML model training, deployment, serving and monitoring.
• Develop and optimize CI/CD pipelines for ML workflows.
• Extend platform capabilities to support LLMOps, RAG and agentic AI workloads.

Skills

Python

CI/CD

Docker

Kubernetes

LLMOps

Model serving

Observability

Kubeflow

Airflow

Metaflow

Prefect

Terraform

Cloud platforms

Communication

Education

Bachelor’s or Master’s in Computer Science or Engineering

Tools

Docker

Kubernetes

Terraform

OpenTelemetry

Langfuse

Job description

We're looking for an MLOps Engineer to join our team in Amsterdam or Rijswijk, Netherlands, 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 offer26 paid holiday daysPension plan schemeDisability insurance (WGA Shortfall insurance)Long-term disability insurance (WIA Top up insurance)EPAM Employee Stock Purchase Plan (ESPP)Commuting to work - costs reimbursementLaptop + corporate simcard + corporate mobile device (subject to certain eligibility requirements)Bike leaseEmployee Assistance ProgramCorporate Programs including Employee Referral Program with rewardsLearning and development opportunities including in-house training and coaching, professional certifications, and courses