Senior Agentic Workflow Engineer — Biologics Discovery

Relha LLC; Northern (KY); Hybrid; USD 105,000 - 175,000; Full time

Job summary

Johnson & Johnson Innovative Medicine seeks a Senior Agentic Workflow Engineer to build the agentic layer of the Biologics Discovery data engine, creating scalable, AI-enabled workflows that connect experiments to models across the DMTL cycle.

You will collaborate with AI/ML teams and discovery scientists to compress cycle time, improve reproducibility, and deliver automated data processing in a lab environment with cross-functional partners.

Qualifications

• Master's or Ph.D. in Computer Science, Engineering, or related field.
• At least 2 years of experience building scientific workflow orchestration.
• Hands-on experience designing real-time data pipelines and integrating diverse data.
• Experience applying agentic AI and modern LLM-based agents to scientific workflows.
• Experience with enterprise GenAI platforms and retrieval-augmented approaches.
• Strong communication skills and comfort operating in ambiguity within a matrixed organization.

Responsibilities

• Build and continuously improve agentic workflows that automate secondary analysis for biologics discovery assays.
• Develop and maintain real-time pipelines connecting automation software, data stores, models, and compute environments.
• Collaborate with IT and platform teams to implement APIs, observability, versioning, and end-to-end orchestration.
• Work with Enterprise Generative AI teams to extend GenAI platforms and agentic frameworks for biologics discovery.
• Ensure automated workflows produce semantically consistent, reusable data with proper provenance.

Skills

Agentic workflows

Data pipelines

LLM agents

Communication skills

Ambiguity tolerance

Education

Master's or PhD

Job description

Johnson & Johnson Innovative Medicine seeks a Senior Agentic Workflow Engineer to build the agentic layer of the Biologics Discovery data engine, creating scalable, AI-enabled workflows that connect experiments to models across the DMTL cycle.

You will collaborate with AI/ML teams and discovery scientists to compress cycle time, improve reproducibility, and deliver automated data processing in a lab environment with cross-functional partners.