Lead Test Framework Architect

Job DescriptionAgilent 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.Responsibilities: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 GitHub 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 OpenTelemetry 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 **.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 namespaces, 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.QualificationsRequired10+ 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 (GitHub 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: ephemeral 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 (GitHub 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.PreferredExperience testing AI/ML, LLM, or agent-based systems: evaluation frameworks (deepeval, promptfoo, LangSmith, Langfuse, Maxim AI, or equivalent), non-deterministic regression, hallucination and grounding scoring, drift detection.Fluency in regulated software validation: 21 CFR Part 11, GxP / GAMP 5, CSA vs. CSV, and IEC ** for medical device or diagnostics software.Context engineering for LLMs: prompt/spec design, RAG, MCP servers, project-memory patterns.Observability tooling in containerized environments, ideally with OpenTelemetry GenAI Semantic Conventions.Performance and load testing at platform scale (k6, Gatling, Locust, or equivalent).Infrastructure-as-code and GitOps applied to test environment provisioning.Background in scientific instrument software, laboratory platforms, or life sciences / diagnostics.We offer (depending on the country you are based in):An independent job in collaboration with good colleagues, in a growth-orientated organizationA true commitment to work/life balanceAn opportunity to learn and growAgilent Result Bonus, Stock Purchase Plan, Life Insurance, Pension, Healthcare, Employee Assistance Program, Holiday, Company activitiesAdditional DetailsThis job has a full time weekly schedule.Our pay ranges are determined by role, level, and location.Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.During the hiring process, a recruiter can share more about the specific pay range for a preferred location.Pay and benefit information by country are available at: Technologies Inc. is an equal opportunity employer.Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.Travel Required:25% of the TimeShift:DayDuration:No End DateJob Function:R&D #J-*****-Ljbffr

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.QualificationsRequired10+ 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 (GitHub 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: ephemeral 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 (GitHub 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.PreferredExperience testing AI/ML, LLM, or agent-based systems: evaluation frameworks (deepeval, promptfoo, LangSmith, Langfuse, Maxim AI, or equivalent), non-deterministic regression, hallucination and grounding scoring, drift detection.Fluency in regulated software validation: 21 CFR Part 11, GxP / GAMP 5, CSA vs. CSV, and IEC ***** for medical device or diagnostics software.Context engineering for LLMs: prompt/spec design, RAG, MCP servers, project-memory patterns.Observability tooling in containerized environments, ideally with OpenTelemetry GenAI Semantic Conventions.Performance and load testing at platform scale (k6, Gatling, Locust, or equivalent). Infrastructure-as-code and GitOps applied to test environment provisioning.Background in scientific instrument software, laboratory platforms, or life sciences / diagnostics.We offer (depending on the country you are based in):An independent job in collaboration with good colleagues, in a growth-orientated organizationA true commitment to work/life balanceAn opportunity to learn and growAgilent Result Bonus, Stock Purchase Plan, Life Insurance, Pension, Healthcare, Employee Assistance Program, Holiday, Company activitiesAdditional DetailsThis job has a full time weekly schedule.Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location.