At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.
Learn more at About the opportunity Johnson & J&J; Innovative Medicine is seeking a Senior ML Engineer for our Biologics Discovery Data Science team. This role builds and operates the integration, deployment, lifecycle management, and governance capabilities that enable machine learning (ML) models and AI solutions developed by partner organizations to run reliably in Biologics Discovery environments. You are the senior team member who closes the gap between model-ready data in our data warehouse and models that serve discovery scientists. This position will be based at one of our office locations in either Spring House, PA (strongly preferred), Titusville, NJ, or Raritan, NJ, USA; No remote option.) Please note that this role is available across multiple countries and may be posted under different requisition numbers to comply with local requirements. R
- Why this role matters The future of AI-native discovery depends on high-quality AI and data solutions that connect scientific data, machine learning models, and agentic workflows. Working closely with data scientists, AI/ML scientists, discovery scientists, and partner organizations at J&J;, you will own the deployment, lifecycle management, access, monitoring, and governance of ML, generative AI, and agentic solutions for discovery workflows. You will help operationalize and scale AI/ML capabilities that transform model-ready data and promising models into reliable, production-grade solutions that accelerate scientific discovery.

Key Responsibilities




AI/ML Operations & Lifecycle Management Build and operate scalable pipelines and interfaces that deliver model-ready data to ML, generative AI, and agentic workflows. Enable closed-loop scientific learning by ensuring newly generated scientific data can be captured, governed, and made available to downstream modeling, evaluation, and agentic workflows. Establish reliable operational capabilities for model deployment, serving, monitoring, access management, and lifecycle management across development and production environments. Implement model, data, and workflow versioning, with reproducible releases, rollback capabilities, and traceability across the AI/ML lifecycle. Reliability, Observability & Scale Establish monitoring, observability, alerting, and performance management practices for ML workflows, deployed models, and AI services. Develop and maintain automated workflows supporting testing, release management, environment management, and operational excellence across AI/ML solutions. Enable AI capabilities to scale with growing scientific data volumes, computational demands, and increasingly autonomous discovery workflows. Monitor model and system behavior in production, including data quality, model performance, drift, latency, reliability, and resource utilization. Partnership & Standards Partner with data scientists, technology teams,



and domain experts to establish reliable integration patterns between scientific data products and AI/ML workflows. Enable ML scientists and AI agents with reproducible training, fine-tuning, evaluation, experimentation, and deployment capabilities. Qualifications Required Degree in Computer Science, Engineering, Data Science, Machine Learning, or a related computational field. Strong proficiency in Python, with experience developing AI/ML workflows for model training, fine-tuning, evaluation, deployment, and serving. Experience with cloud infrastructure and modern data platforms used to support AI/ML workloads. Experience implementing production AI/ML practices, including model versioning, deployment automation, CI/CD, automated testing, observability, monitoring, containers, orchestration technologies, and scalable compute environments. Strong software development and automation practices, with the ability to partner effectively with data scientists, AI/ML practitioners, technology teams, and domain experts. Exposure to real-time/near-real-time pipelines and instrument data integration. Experience working with FAIR data principles, metadata management, data lineage, provenance, and AI-ready data practices. Preferred Skills Artificial Intelligence (AI), Coaching, Cognitive Computing, Critical Thinking, Cross-Functional Collaboration, Curious Mindset, Data Structures, Distributed Computing, Emerging Technologies, Human-Computer Relationships, Machine Learning (ML), Persistence and Tenacity, Program Management, Research and Development, SAP Product Lifecycle Management, Scripting Languages, Technologically Savvy Required Skills The anticipated base pay range for this position is €55,**
- €87,** Benefits In addition to base pay,

we offer
the following benefits*: an annua

📌 Senior Ml Engineer, Biologics Discovery (España)
🏢 Johnson Johnson
📍 España