Senior AI / ML Engineer (MLOps & ML Platform)

Acquism SARL; Amsterdam; Sur place; EUR 90 000 - 120 000; Plein temps

Résumé du poste

Acquism SARL in Amsterdam seeks a Senior AI/ML Engineer to lead end-to-end ML pipelines and ML platform development. You will collaborate with data scientists, engineers and product managers to design, deploy, and operate production-ready ML systems powering Digital and Retail services.

The role emphasizes MLOps, CI/CD, cloud infrastructure, and scalable ML workflows using Azure, AWS, Docker, and Kubernetes. Strong Python expertise and a hands-on attitude are essential.

Qualifications

• 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 (Kubernetes).
• Experience with CI/CD tools (GitHub Actions, Azure DevOps).
• Familiarity with Infrastructure as Code (Terraform).
• Strong communication skills and a collaborative mindset.

Responsabilités

• 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.

Connaissances

ML Engineering

MLOps

DevOps

Data Engineering

Python

Cloud platforms

Azure

AWS

Docker

Kubernetes

CI/CD

Terraform

Communication

Outils

Airflow

FastAPI

Azure ML

Snowflake

Azure Data Lake

GitHub Actions

Azure DevOps

Terraform

Description du poste

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.