QA Test Architect (AI + Healthcare Domain)

Key Responsibilities

• Own and build the AI test-generation capability — design, build, and deploy agentic and LLM-based systems that automatically generate comprehensive test suites (functional, negative, boundary, integration) from requirements documents, with retrieval-augmented domain knowledge, on cloud platforms (AWS/Azure).
• Build the evaluation and quality controls for AI-generated tests — accuracy, coverage, and regression checks that verify the AI outputs meet quality standards before use.
• Drive adoption of AI-assisted testing across state programs — each with its own requirements, stakeholders, and pace of change.
• Serve as the AI subject-matter expert to QA leads, delivery/account teams, and client stakeholders on AI in testing; advise on approach and build-vs-buy.
• Own data governance for AI on regulated test data — PHI/PII data isolation, de-identification, and HIPAA-compliant handling.
• Lead and coordinate a small AI engineering and QA team; partner cross-functionally with testing and account teams to deliver and iterate.

Required Qualifications

• Bachelor's or Master's in Computer Science, Engineering, or a related field — or equivalent professional experience.
• 5+ years in AI/ML, AI product delivery, or software/quality engineering, including hands-on design and deployment of LLM / agentic / retrieval-augmented (RAG) systems on a major cloud platform (AWS preferred).
• Demonstrated track record taking AI solutions from concept to production.
• Software testing / QA automation experience and the ability to make AI testing reliable and measurable.
• Strong stakeholder management and the credibility to serve as an AI SME to QA leaders and clients.
• Experience leading or coordinating technical teams.

Preferred Qualifications

• Healthcare / Medicaid / payer IT domain experience.
• HIPAA / PHI / PII data-governance experience in production systems.
• Test/QA automation tooling and agile delivery.
• Technical stack: Python; LLM APIs (e.g., Anthropic Claude, OpenAI GPT); RAG, embeddings, and vector/semantic search; multi-agent orchestration; cloud (AWS/Azure) and MLOps.