Machine Learning Engineer

Capgemini; Brussel; Sur place; EUR 70 000 - 100 000; Plein temps

Résumé du poste

Capgemini is seeking a Machine Learning Engineer to drive the industrialization of AI solutions. You will work with Data Scientists and IT Production teams to ensure production-ready ML models, robust data pipelines, and scalable infrastructure.

The role emphasizes automation, monitoring, and continuous improvement within Agile delivery environments. Required experience includes 4+ years in ML engineering, containerization of workloads, and familiarity with CI/CD pipelines (GitLab CI).

Qualifications

• Minimum 4 years of relevant experience as a Machine Learning Engineer or similar role.
• Experience with AI platforms and development environments.
• Strong knowledge of containerization and virtualization (Docker, VMs).
• CI/CD pipelines experience, preferably GitLab CI.

Responsabilités

• Collaborate with Data Scientists to define production-ready ML solutions.
• Support infrastructure choices, data ingestion, and API designs for real-time vs batch use cases.
• Industrialize ML pipelines and automate deployment, testing, and monitoring.
• Work with IT Production teams to configure target environments and ensure reliability.

Connaissances

Containerization

CI/CD pipelines

GitLab CI

Model/versioning

Package management

Outils

Docker

VMs

Description du poste

Job Description – Machine Learning Engineer

The Machine Learning Engineer plays a key role in enabling the industrialization of Machine Learning and AI solutions within the enterprise. The mission of the role is to promote and apply best practices in production-ready ML development, ensuring that AI solutions are robust, scalable, monitored, and fully integrated into IT production environments.

Machine Learning Engineers bridge the gap between AI & Analytics teams and IT production, ensuring that Machine Learning models deployed to production are supported by appropriate data pipelines, infrastructure, automation, and monitoring from both a technical and business perspective.

They contribute to the full lifecycle of AI services, from design and development to deployment, monitoring, and continuous improvement.

Key Responsibilities

Machine Learning Engineers contribute to Machine Learning projects by:

• Collaborating closely with Data Scientists to define and develop solutions that meet business requirements while taking production constraints into account (e.g. performance, scalability, latency, data volumes).
• Supporting the selection of appropriate infrastructure and serving models, including data ingestion patterns, synchronization models, and API designs (real-time vs batch processing).
• Contributing to the automation and industrialization of ML pipelines, including:
• Containerization and image creation (Docker / VMs)
• Preparation of unit, regression, and integration tests
• Supporting Data Scientists in the use of existing industrial platforms and CI/CD tools to build, deploy, and monitor AI services.
• Working closely with IT Production teams to support the configuration and parameterization of target environments.
• Ensuring models in production:
• Run reliably and without errors
• Are retrained when required (including automated retraining where applicable)
• Are monitored from both IT (technical) and business (performance, quality) perspectives.

Agile & Delivery Context

The Machine Learning Engineer typically works in Agile delivery environments, contributing within cross-functional teams that combine analytics, engineering, and testing expertise. The role requires close collaboration, continuous feedback, and a strong delivery mindset focused on stable and reusable solutions.

Required Experience & Knowledge

Experience

• Minimum 4 years of relevant experience as a Machine Learning Engineer, ML Platform Engineer, or similar role

Technical Skills

Mandatory

• Strong experience with containerization and virtualization (Docker, VMs)
• Experience with AI platforms and development environments
• CI/CD pipelines, preferably GitLab CI
• Code, data, and model versioning practices
• Package management and dependency management

Preferred

• Model optimization and compression techniques
• ELT / ETL tools
• Data flow processing frameworks
• Data visualization tools

Business & Methodology

Mandatory

• Practical knowledge of Agile methodologies

Language Requirements

• Dutch: nice to have
• French: nice to have

Working Model

Soft Skills

• Strong communication skills (verbal and written)
• Results-driven with a strong sense of ownership
• High attention to detail and rigor
• Creative and analytical problem-solving mindset
• Proactive in continuous learning and knowledge sharing
• Awareness of efficiency and quality of delivery
• Ability to think beyond existing processes and frameworks
• Positive, energetic, and collaborative team player
• Open to change, feedback, and diverse perspectives