Senior ML Engineer – AI/ML Ops for Biologics Discovery

6010-Biosense Webster Inc. Legal Entity; Geel; Sur place; EUR 76 000 - 117 000; Plein temps

Résumé du poste

Johnson & Johnson Innovative Medicine in Beerse, Belgium is seeking a Senior ML Engineer for the Biologics Discovery Data Science team. You will own deployment, lifecycle management, access, monitoring, and governance of ML and AI solutions, partnering with data scientists and discovery scientists to productionize models.

This senior role focuses on operationalizing model-ready data, reproducibility, CI/CD, observability, and scalable compute, ensuring AI capabilities are reliable across

Qualifications

• Required Degree in Computer Science, Engineering, Data Science, Machine Learning, or a related computational field.
• 4+ years of experience operationalizing and scaling AI/ML solutions in production environments.
• Strong proficiency in Python and AI/ML workflows for training, fine-tuning, evaluation, deployment, and serving.

Responsabilités

• AI/ML Operations & Lifecycle Management: build and operate scalable pipelines delivering model-ready data to ML, generative AI, and agentic workflows.
• Reliability, Observability & Scale: establish monitoring, automated testing, and scalable compute for AI solutions.
• Partnership & Standards: collaborate to establish reusable patterns, patterns and standards for AI governance, security, and cost management.

Connaissances

Python

CI/CD

Machine Learning

Automation

Cloud platforms

Formation

Bachelor's or higher in CS/Engineering/Data Science

Outils

MLflow

Weights & Biases

Docker/Kubernetes

Cloud platforms (AWS/GCP/Azure)

Description du poste

Johnson & Johnson Innovative Medicine in Beerse, Belgium is seeking a Senior ML Engineer for the Biologics Discovery Data Science team. You will own deployment, lifecycle management, access, monitoring, and governance of ML and AI solutions, partnering with data scientists and discovery scientists to productionize models.

This senior role focuses on operationalizing model-ready data, reproducibility, CI/CD, observability, and scalable compute, ensuring AI capabilities are reliable across