ML Engineer

Capgemini; Diegem; Hybrid; EUR 70,000 - 110,000; Full time

Job summary

Capgemini in Diegem, Belgium, seeks a Senior Machine Learning Engineer to develop production-grade ML pipelines and integrate solutions into industrial environments. You will collaborate with Data Scientists, select suitable infrastructure, and automate deployment while ensuring monitoring and retraining as needed.

Requirements include advanced Python, CI/CD experience, and strong communication skills. This role offers a balanced 50% onsite/50% remote work arrangement and fluency in English;

Qualifications

• 4+ years of relevant experience in ML engineering or production ML pipelines.
• Experience with containerization and virtualization in industrial settings.
• Experience with CI/CD and code, model and data versioning.
• Strong communication and ability to work with Data Scientists and IT Production.

Responsibilities

• Collaborate with Data Scientists to define production-ready ML solutions.
• Select infrastructure and serving models based on requirements.
• Automate ML pipeline components for production deployment.
• Build Docker images and prepare unit/ integration tests.
• Support IT Production with environment parameterization and monitoring.

Skills

Advanced Python

CI/CD

Data versioning

Model deployment

Team collaboration

Communication

Tools

Docker

VMs

GitLab CI

PostgreSQL

Spark

Job description

Mission Context

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.

Job Description

Machine Learning Engineer - Senior

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.

Language Requirements

• Dutch: Plus
• French: Plus
• English: Mandatory

Telework

50% onsite and 50% homeworking

Required Experience

• Job Description - Grade Specific
• 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
• Ownership and accountability
• Open to change, feedback and collaboration