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# ML Engineer

- **Company:** Capgemini
- **Location:** Diegem (Machelen), Belgium (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Continuous Integration, Data Validation, Extract Transform Load (ETL), Data Visualization, Python (Programming Language), PostgreSQL, Machine Learning, Model Compression, Service Development Studio, Software Deployment, Data Streaming, Management of Software Versions, Apache Spark, Containerization, AI Platforms, Gitlab-ci, Integration Tests, Data Analytics, Data Management, Machine Learning Operations, Docker
- **Published:** November 12, 2026
- **Apply:** https://www.capgemini.com/jobs/592071-en_GB_SAPBTP/x/

## About the Role

At least 4 years of relevant experience

Technical Experience (Mandatory)

Containerization and virtualization

AI platforms and IDEs

CI/CD (GitLab CI)

Code, model and data versioning

Advanced Python

Package and dependency management

PostgreSQL

Technical Experience (Preferred)

Integration technologies including distributed and mainframe environments

Model compression techniques

ELT/ETL tools

Big Data tools such as Spark

Data flow processing

Data visualization tools

Business Experience (Mandatory)

Knowledge of Agile methodology

Soft Skills

Strong written and verbal communication

Results driven

Attention to detail

Creativity and problem solving

Continuous learning mindset

Efficiency and effectiveness awareness

Out-of-the-box thinking

## Description

Machine Learning Engineers promote best practices in industrial code development across the ML & AI community. They develop production-ready ML pipelines and integrate existing ML solutions into industrial environments. They contribute to AI service development, deployment, monitoring, data quality checks, data flow design, model design, and production integration. They also facilitate communication between AI & Analytics teams and IT Production to ensure properly deployed and monitored ML models.

Function Description

Work with Data Scientists to define and develop target solutions while considering production constraints.

Select appropriate infrastructure and serving models based on business requirements and processing needs.

Automate ML pipeline components for production deployment.

Build Docker or VM images and prepare unit, regression, and integration tests.

Support Data Scientists in using industrial AI service and CI/CD solutions.

Support IT Production with environment parameterization.

Ensure models run without errors, are retrained when required, and are monitored from both IT and business perspectives.