Location: Amsterdam, Netherlands
Contract Duration: 6 months (renewable)
Experience: 6–8 years
We are looking for a Senior AI / ML Engineer to join our growing ML Engineering team. You will work closely with data scientists, engineers, and product managers to design, build, deploy, and operate robust machine learning systems that power key services across our Digital and Retail platforms.
This role has a strong focus on MLOps and ML Platform development, helping scale and maintain production-ready ML workflows using modern cloud infrastructure and tooling.
Design, develop, and maintain end-to-end ML pipelines for training, validation, deployment, and monitoring.
Support scalable ML solutions across use cases such as recommendations, forecasting, and automation.
Productionise data science models into reliable, scalable services.
Build and operate ML services using Airflow, Azure ML, and FastAPI.
Automate model deployment and lifecycle management using CI/CD pipelines (GitHub Actions, Azure DevOps).
Improve reliability, observability, and performance of the ML platform.
Implement monitoring, alerting, and model drift detection using tools like Azure Monitor, NewRelic, Grafana, and custom logging.
Manage and evolve infrastructure using Terraform, Docker, and AWS Fargate.
Collaborate cross-functionally with engineering, data, and product teams.
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Proven experience in ML Engineering, MLOps, DevOps, or Data Engineering with exposure to the full ML lifecycle.
Hands-on experience building or maintaining ML pipelines and workflows.
Strong Python skills with experience using MLflow, Scikit-learn, PyTorch, or similar frameworks.
Experience with cloud platforms, particularly Azure and AWS.
Solid understanding of containerization (Docker) and orchestration (e.g. Kubernetes).
Experience with CI/CD tools (GitHub Actions, Azure DevOps).
Familiarity with Infrastructure as Code (Terraform).
Strong communication skills and a collaborative mindset.
Languages: Python (primary), SQL, Bash
Cloud: Azure, AWS
ML & Data: MLflow, Azure ML, Airflow, Snowflake, Delta Lake, Redis, Azure Data Lake
APIs & Services: FastAPI
Infrastructure & Ops: Docker, AWS Fargate, Terraform, GitHub Actions, Azure DevOps, Grafana, Azure Monitor
Experience with data platforms such as Snowflake or Azure Data Lake.
Deploying ML models as APIs or services (FastAPI, Azure Functions).
Strong understanding of model performance monitoring, observability, and drift detection.
