Role Name: Senior AI/ML Engineer

Location: Amsterdam, Netherlands

Role Summary

We are looking for a highly skilled AI/ML Senior Engineer to join our growing ML Engineering team. In this role, you will work closely with data scientists, software engineers, and product managers to design, build, deploy, and operate production-grade machine learning systems that support critical Digital and Retail business use cases.

This position has a strong focus on MLOps and ML Platform Engineering, enabling scalable, reliable, and observable ML workflows using modern cloud infrastructure, CI/CD practices, and automation tools.

Key Responsibilities

• Design, develop, and maintain end-to-end ML pipelines for model training, validation, deployment, and monitoring.
• Build and support scalable ML solutions for use cases such as recommendations, forecasting, and automation.
• Productionize machine learning models using tools such as Airflow, Azure ML, and FastAPI.
• Automate ML model deployment and release processes using CI/CD pipelines (GitHub Actions, Azure DevOps).
• Ensure reliability, performance, observability, and scalability of the ML platform.
• Collaborate with data scientists to convert research prototypes into robust, production-ready services.
• Implement monitoring, alerting, logging, and model drift detection using tools like NewRelic, Azure Monitor, Grafana, and custom frameworks.
• Manage and continuously improve infrastructure using Terraform, Docker, and cloud-native services (Fargate/Kubernetes).
• Follow best practices in DevOps, security, and cloud architecture.

Essential Skills & Experience

• 6–8 years of experience in ML Engineering, DevOps, or Data Engineering, with strong exposure to the full ML lifecycle.
• Hands-on experience building and maintaining ML workflows and pipelines.
• Strong programming skills in Python.
• Experience with ML frameworks and tools such as MLflow, Scikit-learn, PyTorch.
• Solid understanding of cloud platforms, especially Azure and AWS.
• Experience with containerization and orchestration (Docker, Kubernetes).
• Exposure to CI/CD tools (GitHub Actions, Azure DevOps).
• Experience with Infrastructure as Code (Terraform).
• Strong communication skills and ability to work effectively in cross-functional teams.

Technical Stack

Languages

• Python (Primary)
• SQL
• Bash

Cloud Platforms

• Microsoft Azure
• Amazon Web Services (AWS)

ML & Application Tools

• MLflow
• Azure ML
• Apache Airflow
• FastAPI
• Docker
• Fargate

Data Platforms

• Snowflake
• Delta Lake
• Redis
• Azure Data Lake

Infrastructure & DevOps

• Terraform
• GitHub Actions
• Azure DevOps
• Grafana
• Azure Monitor

Desirable Skills

• Experience working with enterprise data platforms such as Snowflake and Azure Data Lake.
• Experience deploying ML models as APIs or microservices (FastAPI, Azure Functions).
• Knowledge of model performance monitoring, observability, and drift detection best practices.
• Familiarity with workflow orchestration tools such as Airflow or Azure Data Factory.