Develop AI/LLM application services for the controlled AI Harness, including prompt pipelines, RAG services, workflow orchestration, evidence generation, and reviewer-facing outputs.
Build and maintain read-only integrations to enterprise systems such as DOORS, ALM, ClearQuest, Windchill, SOP/document repositories, and DHF repositories.
Implement AI agents for requirement-quality checks, traceability gap detection, DHF completeness review, defect/change impact analysis, compliance checks, and draft evidence generation.
Create and maintain prompt libraries, retrieval pipelines, metadata schemas, source-citation mechanisms, and output templates aligned with approved QMS workflows.
Implement audit logging, model routing, access control, project isolation, and evidence packaging needed for controlled and traceable AI usage.
Support OQ/PQ activities by building test harnesses, representative datasets, prompt regression tests, hallucination checks, and workflow validation evidence.
Partner with SMEs, QA, RA, Software Quality, and Validation teams to refine use cases, acceptance criteria, and reviewer workflows.
Optimize system performance, reliability, and observability across model calls, document processing, retrieval quality, and application integrations.
Maintain technical documentation, developer documentation, and configuration records to support controlled deployment and ongoing change management.
Contribute to pilot rollouts, user feedback loops, and incremental expansion of the AI Harness to new use cases and product teams.
You have acquired 3+ years of experience in software engineering, AI application development, LLM integration, workflow automation, or enterprise systems integration.
Your skills include strong programming experience in Python, plus practical experience with APIs, backend services, structured data processing, and AI/LLM application development.
You have hands-on experience building RAG pipelines, prompt-based applications, evaluation workflows, or agent-style systems for business or engineering use cases.
You are familiar with vector search, document chunking, metadata filtering, citation generation, and prompt/version management.
You have experience working with cloud AI platforms, model APIs, or local/private inference environments in enterprise settings.
You understand good engineering practices for logging, testing, reproducibility, access control, and deployment in controlled environments.
You can work effectively with domain SMEs and translate business or QMS requirements into usable technical workflows and reviewer-facing outputs.
You have strong written and verbal communication skills and can document technical solutions clearly for engineering and quality stakeholders.