Lead Test Framework Architect; M​/F​/D

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Job Description Agilent is building a Kubernetes-based software platform spanning instruments, applications, and shared services across multiple product lines. We are embedding AI into how we build and verify software. We are looking for a Lead Test Framework Architect to lead the adoption of AI in our software test lifecycle, and to build the test frameworks, evaluation methods, and governance that let us ship AI-accelerated software safely in regulated, GxP / 21 CFR Part 11 environments.

This is a hands‑on senior individual‑contributor role. AI accelerates software delivery, but that acceleration exposes weaknesses downstream without a strong automated‑testing control system. Building that control system - and leading the organization to adopt it - is the core of this role.

• Lead AI SDLC adoption for quality engineering.
• Own the strategy for AI‑assisted test authoring, execution, triage, and maintenance.
• Prove value through reference workflows, then scale adoption across product teams.
• Build AI‑native test frameworks.
• Design workflows that generate unit, contract, integration, and E2E suites from specifications and intent.
• Verify that AI‑generated tests exercise specified intent, not implementation.
• Integrate Git Hub Copilot (enterprise standard) and complementary tools (e.g., Qodo, Diffblue, Playwright AI agents, self‑healing frameworks) into CI/CD quality gates.
• Test our AI features.
• Build evaluation and regression frameworks for LLM‑and agent‑powered product capabilities: non‑deterministic evaluation, hallucination and grounding scoring, drift detection, and AI observability using Open Telemetry GenAI conventions.
• Own the regulatory bridge.
• Integrate AI‑assisted testing into validated environments using risk‑based Computer Software Assurance (CSA), GAMP 5 (2nd Edition) and the ISPE GAMP AI Guide, and - for diagnostics software - IEC 62304.
• Preserve audit trails, traceability, and data integrity for AI‑generated artifacts.
• Establish human‑in‑the‑loop governance.
• Define risk‑scaled review checkpoints, acceptance criteria, and rollback triggers for AI‑generated code and tests.
• Define platform‑wide test architecture standards for a Kubernetes‑based microservices platform: testability‑by‑design, test environment strategy (ephemeral name spaces, environment‑as‑code), CI/CD quality gate architecture, contract testing for microservices and APIs, test data strategy (synthetic data, isolation, AI/ML data requirements), and E2E frameworks that work reliably in containerized environments.
• Instrument and report on impact.
• DORA delivery metrics augmented with AI‑specific measures (coverage delta, defect‑escape rate, mean‑time‑to‑triage, eval‑suite pass rates).
• Lead the change effort - champions, enablement, playbooks - that drives durable adoption.

• 10+ years of software engineering, with 3+ years in test architecture or quality engineering architecture roles.
• Demonstrated daily, hands‑on use of AI‑assisted development and testing tools (Git Hub Copilot, Claude Code, Cursor, or equivalent) with the ability to speak concretely to effective usage patterns and failure modes.
• Deep hands‑on Kubernetes expertise for test environment design: ephemer namespace provisioning, container‑based test infrastructure, environment‑as‑code.
• Strong experience designing multi‑level test automation frameworks: unit, contract, integration, E2E, and performance testing in distributed systems.
• CI/CD pipeline architecture and quality gate integration (Git Hub Actions, Jenkins, or equivalent).
• Contract testing experience (Pact or consumer‑driven contract testing) for microservices and API‑first architectures.
• Demonstrated ability to deliver reusable test frameworks adopted across multiple product teams — reference implementations, not just guidelines.
• Working knowledge of testability‑by‑design and the ability to influence how software is architected to be inherently more testable.
• Experience leading adoption and change in an established engineering organization, with a track record of defensible outcome metrics.

• testing AI/ML, LLM, or agent‑based systems: evaluation frameworks (deepeval,…