Hybrid MLOps Engineer - LLMOps & AI Infra (NL)

EPAM Systems Inc; Rijswijk; Hybride; EUR 80 000 - 110 000; Plein temps

Avantages offerts par ce poste

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

Résumé du poste

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.

Responsabilités

• 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.

Connaissances

Python

CI/CD

Docker

Kubernetes

LLMOps

Model serving

Observability

Kubeflow

Airflow

Metaflow

Prefect

Terraform

Cloud platforms

Communication

Formation

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

Outils

Docker

Kubernetes

Terraform

OpenTelemetry

Langfuse

Description du poste

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.