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# Principal Agentic Workflow & AI Engineer, Biologics Discovery

- **Company:** Johnson & Johnson
- **Location:** Spring House, PA, United States
- **Experience:** Experienced
- **Salary:** $117,000.0 - $201,250.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Information Engineering, Data Stores, DevOps, Cyber-physical Systems, Robotic Automation Software, Management of Software Versions, Workflow Management Systems, Data Processing, Large Language Models, Generative AI, Build Management, Information Technology, Machine Learning Operations, Data Pipelines
- **Published:** September 3, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3375480840&tx=DT10394UYZ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Advanced degree (Ph.D. preferred) in Computer Science, Engineering, or a related computational field.
 * At least 3 years experience building scientific workflow orchestration, automation software, including experience in agentic workflows and technical leadership responsibilities.
 * 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 building on enterprise or shared GenAI platforms and retrieval-augmented approaches (e.g., RAG or GraphRAG) rather than standing up capabilities from scratch.
 * Ability to translate scientific objectives into practical, scalable technical solutions and to set technical direction for others.
 * Strong communication and stakeholder management with comfort operating in ambiguity and representing work to leadership in matrixed organizations.

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

This position will be located at one of our office locations in either Spring House, PA (preferred), Titusville, NJ, or Raritan, NJ. (No remote option.)

## Description

Johnson & Johnson Innovative Medicine is seeking a Principal Agentic Workflow & AI Engineer to help build the agentic layer of our Biologics Discovery data engine. This is an opportunity to be one of the early, senior experts shaping how agentic AI workflows 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.

This position will be located at one of our office locations in either Spring House, PA (preferred), Titusville, NJ, or Raritan, NJ. (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 agentic AI and intelligent automation to compress cycle time, reduce manual intervention, and improve reproducibility - transforming today's hands-on processes into scalable, connected, intelligent discovery workflows. It is a strategically visible Principal-level role, central to our next-generation discovery automation.

Why This Role Is Unique

This is a rare chance to architect how scientific and physical AI come together to steer design and execution in the lab - not just automating steps but building a next-generation discovery engine in a large pharma setting. As a Principal individual contributor, your work will be among the most visible and influential on the team and will directly accelerate how tomorrow's therapies are discovered., As a Principal Agentic Workflow & AI Engineer, you will design and build agentic workflows and orchestration that own the scientific data analysis and secondary processing logic our team delivers. You will translate scientific priorities into automation and AI roadmaps, and build real-time pipelines connecting automation software, data stores, models, and compute.

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 enable closed-loop feedback so that each experimental cycle improves downstream models and decisions - timed to lab-automation readiness as new automation infrastructure comes online. You will also set technical direction and mentor other team members., Agentic Orchestration & Integration

 * Design, configure, and continuously improve agentic workflows that automate secondary analysis for biologics discovery assays, replacing fragile manual and ad-hoc scripted steps.
 * Build or oversee 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.
 * Partner 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

 * Design workflows optimized for AI-driven learning, not just throughput, enabling 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.
 * Ensure data generated through agentic workflows is high-quality, traceable, interoperable, and AI-ready, with strong metadata, provenance, and lineage.
 * Identify opportunities to apply agentic AI to compress cycle time and improve reproducibility.

Technical Leadership

 * Set technical direction and standards for agentic workflows and mentor engineers and data scientists across the AI/ML space.
 * Evaluate and pilot emerging agentic AI methods, frameworks, and tools, guiding where the team invests.
 * Represent the team's agentic work in cross-functional technical forums, aligning approaches with partner teams and enterprise standards.


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