Senior Machine Learning Engineer

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. R&D Machine Learning Beerse, Antwerp, Belgium, Madrid, Spain, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America Johnson & Johnson 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. The future of AI-native discovery depends on high-quality AI and data solutions that connect scientific data, machine learning models, and agentic workflows. In this role, you will enable AI/ML solutions to move reliably from development into production within Biologics Discovery. 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. Your work will enable reliable, production-grade AI workflows and accelerate the adoption of ML and agentic systems in biologics discovery. 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. 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. 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. 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. Establish reusable patterns, best practices, and standards that accelerate the transition from experimentation to production deployment. Contribute to security, access control, AI governance, documentation, and cost management practices across AI/ML solutions. 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, including ML, generative AI, or agentic workflows. ~ 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. 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.) All qualified applicants will receive consid