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# Senior Machine Learning Engineer

- **Company:** Capgemini
- **Location:** Brussel, Belgium
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Continuous Integration, Extract Transform Load (ETL), Data Visualization, Python (Programming Language), PostgreSQL, Machine Learning, Package Management Systems, Data Streaming, Management of Software Versions, Virtualization Technology, Data Ingestion, Apache Spark, Containerization, AI Platforms, Gitlab-ci, Integration Tests, Data Management, Docker
- **Published:** September 12, 2026
- **Apply:** https://www.adzuna.be/details/5879867538

## About the Role

At least 4 years of relevant experience Technical experience:

Mandatory:

 * Containerization / Virtualization

 * AI platforms & IDEs

 * CI/CD (Gitlab-ci)

 * Code, model & data versioning

 * Python advanced

 * Usage of package management tools and experience in dependency management

 * PostgreSQL

 * Knowledge of agile methodology

Preferable:

 * Experience with integration using different technologies (distributes/mainframe) and infra components

 * Model compression techniques

 * ELT / ETL tools

 * Big data tools (Spark)

 * Data flow processing

 * Data visualization tools Business experience

## Description

* Working with the Data Scientists to define and develop the target solution with production constraints in mind. This allows to select the correct run infrastructure and serving model (e.g. data ingestion scheme, API synchronicity, …) to address the business requirements (real-time responses, processing volumetry, …)

 * Contributing to the automation of the different elements of the ML pipeline in order to integrate and deploy them in the production environment (e.g. building Docker/VM images, prepare unitary, regression and integration tests, …)

 * Supporting Data Scientists on the usage of the existing industrial solutions available to build and monitor AI services (i.e. the CI/CD tools)

 * Supporting IT Production on the parameterization of the target environment Ensuring that the model runs without errors, is retrained if needed (incl. automatically) and is monitored both from the IT and the business perspective.


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