Senior Agentic Workflow Engineer, Biologics Discovery
About The Position
Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Johnson & Johnson Innovative Medicine is seeking a Senior Agentic Workflow Engineer to help build the agentic layer of our Biologics Discovery data engine. This is an opportunity to be one of the early builders of agentic AI workflows that automate secondary data processing, orchestrate analyses, and connect experimental outputs to models and decisions across the DMTL (design-make-test-learn) cycle. You will work at the intersection of scientific experimentation, data pipelines, AI systems, and lab automation, partnering closely with the broader AI/ML team to bring these systems to life. Getting biologics data to flow reliably through our systems is a critical, often underestimated challenge, and there is significant opportunity to automate secondary analysis. This role brings hands-on agentic AI engineering to compress cycle time, reduce manual intervention, and improve reproducibility - building the scalable, connected, intelligent discovery workflows that today's hands-on processes still lack. This is a rare chance to help build how scientific and physical AI come together to steer design and execution in the lab - contributing hands-on to one of the first agentic discovery engines in a large pharma setting. As the team and its agentic capabilities grow, this role offers a clear path to increasing scope, technical ownership, and influence.
Requirements
- Master's or Ph.D. in Computer Science, Engineering, or a related computational field (or Bachelor's with substantial relevant experience).
- At least 2 years experience building scientific workflow orchestration, or automation software, including experience in agentic workflows.
- Hands-on experience designing real-time or near-real-time data pipelines and integrating complex and heterogeneous scientific data and instrument outputs.
- Experience applying agentic AI and modern LLM-based agents (or comparable intelligent-automation systems) to scientific or laboratory workflows.
- Experience working with enterprise or shared GenAI platforms and retrieval-augmented approaches (e.g., RAG or GraphRAG) rather than standing up capabilities from scratch.
- Strong communication skills and comfort operating in ambiguity within a matrixed organization.
Nice To Haves
- Experience in drug discovery domains such as biologics, high-throughput experimentation, or imaging.
- Exposure to lab automation, robotic systems, or cyber-physical systems for R&D.
- Familiarity with MLOps/DevOps, workflow engines, and production-grade monitoring/observability.
- Understanding of FAIR data, ontologies, semantic models, lineage/provenance, and AI-ready data standards.
Responsibilities
- Build and continuously improve agentic workflows that automate secondary analysis for biologics discovery assays, replacing fragile manual and ad-hoc scripted steps.
- Develop and maintain real-time or near-real-time pipelines connecting automation software, data stores, models, and compute environments.
- Partner with IT and platform teams to implement resilient APIs, observability, versioning, and workflow orchestration end-to-end.
- Work with our Enterprise Generative AI team to build on shared GenAI platforms, models, and agentic frameworks - extending them for biologics discovery rather than duplicating enterprise capabilities.
- Partner with ontology and MLOps colleagues so automated workflows produce semantically consistent, reusable data and deploy reliably into production.
- Contribute to workflows optimized for AI-driven learning, not just throughput, supporting closed-loop feedback across the design-make-test-learn (DMTL) cycle.
- Collaborate with discovery scientists, AI/ML scientists, and data engineers so experimental outputs improve downstream property models and decision-making.
- Help ensure data generated through agentic workflows is high-quality, traceable, interoperable, and AI-ready, with strong metadata, provenance, and lineage.
- Identify and propose opportunities to apply agentic AI to compress cycle time and improve reproducibility.
Benefits
- medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, and group legal insurance.
- consolidated retirement plan (pension) and savings plan (401(k)).
- Vacation – up to 120 hours per calendar year
- Sick time - up to 40 hours per calendar year
- Holiday pay, including Floating Holidays – up to 13 days per calendar year
- Work, Personal and Family Time - up to 40 hours per calendar year
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What This Job Offers
Job Type
Full-time
Career Level
Senior
