Senior AI/ML Ops Engineer – Biologics Discovery

Johnson & Johnson Innovative Medicine; Beerse; Sur place; EUR 76 000 - 117 000; Plein temps

Avantages offerts par ce poste

Annual bonus

Résumé du poste

Johnson & Johnson Innovative Medicine is seeking a Senior ML Engineer to join the Biologics Discovery Data Science team in Beerse, Belgium. You will build and operate ML/AI deployment, governance, and lifecycle capabilities to productionize partner-developed models for discovery scientists.

You will lead modern AI/ML operations, emphasizing reproducibility, CI/CD, observability, and scalable compute, ensuring reliable model serving and data workflows in biologics discovery.

Qualifications

• Degree in Computer Science, Engineering, Data Science, or related field.
• 4+ years in production AI/ML, including ML, generative AI, or agentic workflows.
• Strong Python development experience for ML workflows.
• Experience with cloud infrastructure and data platforms.
• Expertise with ML lifecycle tools and model/versioning.

Responsabilités

• Enable AI/ML solutions moving from development to production in Biologics Discovery.
• Own deployment, lifecycle management, access, monitoring, and governance of ML/AI solutions.
• Collaborate with data scientists and domain experts to operationalize models.
• Implement CI/CD, reproducibility, and monitoring for production pipelines.
• Develop reusable patterns and standards for scalable AI solutions.

Connaissances

Python

ML Ops

CI/CD

Observability

Automation

Formation

Bachelor's degree in Computer Science or related field

Master's preferred

Outils

MLflow

Weights & Biases

Cloud platforms

Containerization (Docker/K8s)

Description du poste

Johnson & Johnson Innovative Medicine is seeking a Senior ML Engineer to join the Biologics Discovery Data Science team in Beerse, Belgium. You will build and operate ML/AI deployment, governance, and lifecycle capabilities to productionize partner-developed models for discovery scientists.

You will lead modern AI/ML operations, emphasizing reproducibility, CI/CD, observability, and scalable compute, ensuring reliable model serving and data workflows in biologics discovery.