MLOps Engineer

We are looking for an MLOps Engineer who knows that a model is only as good as the pipeline behind it — someone who has actually kept ML systems running in production, not just deployed a tutorial to a notebook. You will work on international projects for clients in banking, insurance, and telco (US, Netherlands, UK), building the infrastructure that makes AI reliable at scale.

• Proven experience running ML/AI systems in production — you’ve dealt with model drift, pipeline failures, and scaling issues in real environments
• Strong Python skills and hands-on experience with MLOps tooling: MLflow, Kubeflow, Airflow, or similar
• Solid experience with containerization (Docker) and orchestration (Kubernetes) in production settings
• Working knowledge of at least one major cloud platform (AWS SageMaker, Azure ML, or GCP Vertex AI) and its ML services
• Experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins, or Azure DevOps) applied to ML workflows
• Infrastructure as Code experience (Terraform, Pulumi, or CloudFormation)
• Understanding of ML fundamentals — you don’t need to build models, but you need to understand what makes them break in production
• Experience with monitoring and observability tools (Prometheus, Grafana, Datadog, or similar)
• English B2+ — client-facing role, calls and written communication included

Nice to have:

• Experience with LLM serving infrastructure (vLLM, TGI, Triton Inference Server)
• Databricks, Azure AI Foundry, or AWS Bedrock
• GPU infrastructure management and cost optimization
• Kafka or streaming pipelines for real-time inference
• Experience with vector databases (Qdrant, Weaviate, Pinecone, pgvector) in production RAG setups
• Familiarity with AI governance and regulatory context (EU AI Act, GDPR)

• Designing, building, and maintaining CI/CD pipelines for ML model training, evaluation, and deployment
• Managing model lifecycle end-to-end — from experiment tracking and versioning to production serving and monitoring
• Setting up and maintaining infrastructure for ML workloads on cloud platforms (AWS, Azure, or GCP)
• Implementing monitoring, alerting, and observability for deployed models — detecting drift, latency issues, and quality degradation
• Building and managing feature stores, data pipelines, and ETL processes that feed ML models
• Containerizing and orchestrating ML services using Docker and Kubernetes
• Collaborating with data scientists and ML engineers to streamline the path from experimentation to production
• Implementing Infrastructure as Code (Terraform, Pulumi, or CloudFormation) for reproducible ML environments
• Defining and enforcing MLOps best practices, standards, and documentation across teams