Senior Machine Learning Engineer

Stott and May; Brussel; Sur place; EUR 60 000 - 100 000; Plein temps

Résumé du poste

Stott and May is seeking an experienced Machine Learning Engineer to join a large-scale AI and data transformation programme. You will design, deploy and optimise production-grade ML solutions within a modern cloud and data environment.

Working alongside Data Scientists, Data Engineers and IT teams, you will take machine learning models from concept through to production, ensuring they are scalable, automated and fully monitored throughout their lifecycle.

Qualifications

• Minimum 4 years' commercial experience as a Machine Learning Engineer or MLOps Engineer.

Responsabilités

• Design, build and deploy production-ready machine learning pipelines.
• Work closely with Data Scientists to develop scalable ML solutions that meet business and technical requirements.
• Build and maintain CI/CD pipelines for machine learning deployments.
• Develop and manage containerised ML applications using modern virtualisation technologies.
• Implement robust model monitoring, retraining and performance optimisation processes.
• Design and maintain data pipelines to support AI services and model deployment.
• Ensure high standards of code quality, version control and dependency management.
• Support production environments by troubleshooting, monitoring and improving deployed ML services.
• Collaborate with cross-functional teams including Data Science, Engineering, Infrastructure and Operations.
• Drive best practices across MLOps, automation and industrialised machine learning delivery.

Connaissances

Python

CI/CD

Agile

Distributed systems

Model optimization

Data visualization

Outils

Docker

Kubernetes

GitLab CI

PostgreSQL

Description du poste

We are looking for an experienced Machine Learning Engineer to join a large-scale AI and data transformation programme. This is an exciting opportunity to help design, deploy and optimise production-grade machine learning solutions within a modern cloud and data environment.

Working alongside Data Scientists, Data Engineers and IT teams, you will play a key role in taking machine learning models from concept through to production, ensuring they are scalable, automated and fully monitored throughout their lifecycle.

Responsibilities

• Design, build and deploy production-ready machine learning pipelines.
• Work closely with Data Scientists to develop scalable ML solutions that meet business and technical requirements.
• Build and maintain CI/CD pipelines for machine learning deployments.
• Develop and manage containerised ML applications using modern virtualisation technologies.
• Implement robust model monitoring, retraining and performance optimisation processes.
• Design and maintain data pipelines to support AI services and model deployment.
• Ensure high standards of code quality, version control and dependency management.
• Support production environments by troubleshooting, monitoring and improving deployed ML services.
• Collaborate with cross-functional teams including Data Science, Engineering, Infrastructure and Operations.
• Drive best practices across MLOps, automation and industrialised machine learning delivery.

Required Skills & Experience

• Minimum 4 years' commercial experience as a Machine Learning Engineer or MLOps Engineer.
• Advanced Python development skills.
• Strong experience with containerisation technologies (Docker/Kubernetes or similar).
• Experience building and maintaining CI/CD pipelines (GitLab CI or equivalent).
• Experience with model, code and data versioning.
• Strong knowledge of package and dependency management.
• Experience working with PostgreSQL.
• Strong understanding of Agile delivery methodologies.
• Apache Spark or other big data technologies.
• Data flow processing.
• Integration across distributed systems and enterprise platforms.
• Model optimisation and compression techniques.
• Data visualisation tools.
• Experience deploying AI solutions into enterprise production environments.