Senior AI / ML Engineer (MLOps & ML Platform)

Amsterdam, Netherlands | Posted on 02/19/2026

Location:

Amsterdam, Netherlands

Contract Duration: 6 months (renewable)

Experience:

6–8 years

About the Role

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.

Key Responsibilities

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.

Requirements

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.

Technical Stack

APIs & Services:

FastAPI

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 .

#J-18808-Ljbffr