Principal Agentic Workflow & AI Engineer, Biologics Discovery
Johnson & Johnson
September 01, 2026
On-site
Spring House, PA
Clinical Research and Development
About The Opportunity
Johnson & Johnson Innovative Medicine is seeking a Principal Agentic Workflow & AI Engineer to build the agentic layer of our Biologics Discovery data engine, automating secondary data processing, orchestrating analyses, and connecting experimental outputs to models and decisions across the DMTL (design-make-test-learn) cycle.
Position Summary
- Design and build agentic workflows and orchestration owning scientific data analysis and secondary processing logic.
- Translate scientific priorities into automation/AI roadmaps.
- Build real-time pipelines connecting automation software, data stores, models, and compute.
- Enable closed-loop feedback across Discovery, Data Science, In Silico Discovery, Enterprise Generative AI, and data infrastructure/lab automation.
- Set technical direction and mentor team members.
Key Responsibilities
- Agentic Orchestration & Integration: automate secondary analysis; build/oversee real-time pipelines; implement resilient APIs, observability, versioning, orchestration; extend shared GenAI platforms for biologics discovery; ensure semantic consistency and reliable production deployment.
- AI-Driven Scientific Learning: optimize for closed-loop learning across DMTL; collaborate to improve property models/decisions; ensure high-quality, traceable, interoperable, AI-ready outputs with strong metadata/provenance/lineage; improve cycle time and reproducibility.
- Technical Leadership: set standards; evaluate/pilot agentic AI methods/frameworks; represent work in cross-functional forums.
Qualifications
Required:
- Ph.D. preferred in CS/Engineering or related computational field.
- 3+ years building scientific workflow orchestration/automation software with agentic workflow experience and technical leadership.
- Hands-on real-time/near-real-time pipeline design integrating heterogeneous scientific/instrument data.
- Experience applying agentic AI/LLM agents to scientific/lab workflows.
- Experience using enterprise/shared GenAI platforms and RAG/GraphRAG.
- Ability to translate scientific objectives into scalable solutions; strong communication in matrixed organizations.
Preferred:
- Drug discovery (biologics), high-throughput experimentation, or imaging.
- Lab automation/robotics/cyber-physical systems exposure.
- MLOps/DevOps, workflow engines, and production monitoring/observability.
- FAIR/ontologies/semantic models, lineage/provenance, AI-ready data standards.
Location/Remote
Spring House, PA (preferred), Titusville, NJ, or Raritan, NJ. No remote option.
Johnson & Johnson Innovative Medicine is seeking a Principal Agentic Workflow & AI Engineer to build the agentic layer of our Biologics Discovery data engine, automating secondary data processing, orchestrating analyses, and connecting experimental outputs to models and decisions across the DMTL (design-make-test-learn) cycle.
Position Summary
- Design and build agentic workflows and orchestration owning scientific data analysis and secondary processing logic.
- Translate scientific priorities into automation/AI roadmaps.
- Build real-time pipelines connecting automation software, data stores, models, and compute.
- Enable closed-loop feedback across Discovery, Data Science, In Silico Discovery, Enterprise Generative AI, and data infrastructure/lab automation.
- Set technical direction and mentor team members.
Key Responsibilities
- Agentic Orchestration & Integration: automate secondary analysis; build/oversee real-time pipelines; implement resilient APIs, observability, versioning, orchestration; extend shared GenAI platforms for biologics discovery; ensure semantic consistency and reliable production deployment.
- AI-Driven Scientific Learning: optimize for closed-loop learning across DMTL; collaborate to improve property models/decisions; ensure high-quality, traceable, interoperable, AI-ready outputs with strong metadata/provenance/lineage; improve cycle time and reproducibility.
- Technical Leadership: set standards; evaluate/pilot agentic AI methods/frameworks; represent work in cross-functional forums.
Qualifications
Required:
- Ph.D. preferred in CS/Engineering or related computational field.
- 3+ years building scientific workflow orchestration/automation software with agentic workflow experience and technical leadership.
- Hands-on real-time/near-real-time pipeline design integrating heterogeneous scientific/instrument data.
- Experience applying agentic AI/LLM agents to scientific/lab workflows.
- Experience using enterprise/shared GenAI platforms and RAG/GraphRAG.
- Ability to translate scientific objectives into scalable solutions; strong communication in matrixed organizations.
Preferred:
- Drug discovery (biologics), high-throughput experimentation, or imaging.
- Lab automation/robotics/cyber-physical systems exposure.
- MLOps/DevOps, workflow engines, and production monitoring/observability.
- FAIR/ontologies/semantic models, lineage/provenance, AI-ready data standards.
Location/Remote
Spring House, PA (preferred), Titusville, NJ, or Raritan, NJ. No remote option.