Senior ML Engineer, Biologics Discovery

About the role

Johnson & Johnson Innovative Medicine is looking for a Senior ML Engineer for its Biologics Discovery Data Science team. The role builds and runs the integration, deployment, lifecycle management and governance capabilities that let ML models and AI solutions built by partner organizations run reliably in Biologics Discovery environments. It bridges the gap between model-ready data in the data warehouse and models used by discovery scientists. The position is based in Madrid, Spain (no remote option).

Key responsibilities

• Build and operate scalable pipelines and interfaces that deliver model-ready data to ML, generative AI and agentic workflows.
• Ensure new scientific data is captured, governed and available for downstream modeling, evaluation and agentic workflows.
• Set up reliable deployment, serving, monitoring, access management and lifecycle management across development and production.
• Implement model, data and workflow versioning with reproducible releases, rollback and traceability.
• Establish monitoring, observability, alerting and performance management for ML workflows and deployed models.
• Develop automated workflows for testing, release management and environment management, and monitor data quality, model performance, drift, latency and resource usage.
• Partner with data scientists, technology teams and domain experts to set integration patterns, standards and best practices, and contribute to security, access control, AI governance, documentation and cost management.

Required profile

• 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, including ML, generative AI or agentic workflows.
• Strong Python skills for building AI/ML workflows covering training, fine-tuning, evaluation, deployment and serving.
• Experience with cloud infrastructure and modern data platforms for AI/ML workloads.
• Expertise with model registries, experiment tracking and ML lifecycle management tools (e.g. MLflow, Weights & Biases).
• Experience with production AI/ML practices: model versioning, deployment automation, CI/CD, automated testing, observability, monitoring, containers, orchestration and scalable compute.
• Preferred: experience in pharma, biotech or life sciences; real-time pipeline and instrument data integration; FAIR data principles, metadata management and data lineage.