Senior Agentic Workflow Engineer, Biologics Discovery

Senior Agentic Workflow Engineer

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.

This position will be based at one of our office locations in either Spring House, PA (strongly preferred), Titusville, NJ, Raritan, NJ, or Cambridge, MA, USA; or at our office in Madrid, Spain. (No remote option.)

Why this role matters:
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.

Why This Role Is Unique

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.

Position Summary

As a Senior Agentic Workflow Engineer, you will build and maintain agentic workflows and orchestration that support the scientific data analysis and secondary processing logic our team delivers. You will implement automation and AI pipelines connecting automation software, data stores, models, and compute, working closely with senior engineers to translate scientific priorities into working systems.

You will partner across Discovery, Data Science, In Silico Discovery (ISD), our Enterprise Generative AI team, and our data-infrastructure and lab-automation teams to help enable closed-loop feedback so that each experimental cycle improves downstream models and decisions.

Key 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.

AI-Driven Scientific Learning

• 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.

Qualifications

Required

• 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.

Preferred

• 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/Dev Ops, workflow engines, and production-grade monitoring/observability.
• Understanding of FAIR data, ontologies, semantic models, lineage/provenance, and AI-ready data standards.