Lead Test Framework Architect (M/F/D)

Agilent Technologies; Barcelona; In loco; EUR 110.000 - 150.000; Tempo pieno

Vantaggi offerti da questo lavoro

Independent job in collaboration with/

Work-life balance

Opportunity to learn and grow

Performance-based bonus

Descrizione del lavoro

Agilent Technologies is building a Kubernetes-based software platform with AI acceleration across instruments, applications, and shared services. We seek a Lead Test Framework Architect to own AI-assisted testing in regulated GxP/21 CFR Part 11 environments, with hands-on depth in Kubernetes, AI evaluation, and validated software practices.

You will lead the setup of AI-native test frameworks, drive governance, and ensure audit trails and data integrity for AI-generated artifacts while scaling

Competenze

• 10+ years of software engineering, with 3+ years in test architecture or quality engineering architecture roles.
• Hands-on use of AI-assisted development and testing tools (e.g., GitHub Copilot, Claude Code, Cursor).
• Deep Kubernetes expertise for test environment design: ephemeral spaces, container-based testing, environment-as-code.
• Experience designing multi-level test automation: unit, contract, integration, E2E, and performance tests.
• CI/CD pipeline architecture and quality gates (GitHub Actions, Jenkins).
• Contract testing experience (Pact or consumer-driven testing) for microservices/APIs.
• Deliver reusable test frameworks adopted across product teams, not just guidelines.
• Working knowledge of testability-by-design to influence architecture for testability.
• Experience leading adoption and change with measurable outcomes.

Mansioni

• Lead AI SDLC adoption for quality engineering and scale AI-assisted test authoring, execution, and maintenance.
• Build AI-native test frameworks and CI/CD quality gates with integrated AI tools.
• Develop evaluation and regression frameworks for LLM- and agent-powered capabilities, including non-deterministic evaluation and drift detection.
• Own the regulatory bridge, integrating AI-assisted testing into validated environments with CSA, GAMP 5, and IEC 62304 where applicable.
• Establish human-in-the-loop governance with risk-based review checkpoints and rollback triggers.
• Define platform-wide test architecture standards for a Kubernetes-based microservices platform and environment-as-code strategies.
• Instrument and report on impact with AI-specific metrics and lead organizational change for durable adoption.

Conoscenze

AI-assisted testing

Kubernetes test env design

Test automation frameworks

CI/CD integration

Contract testing

Leadership in engineering

Observability & OpenTelemetry

Strumenti

GitHub Copilot

Qodo

Diffblue

Playwright AI

OpenTelemetry

Descrizione del lavoro

Job Description

Agilent is building a Kubernetes-based software platform spanning instruments, applications, and shared services across multiple product lines, while embedding AI into how we build and verify that software. That software runs instruments used in over 265,000 labs worldwide, supporting work like R&D, cancer diagnostics, and food and water safety testing.

Job Description

Agilent is building a Kubernetes-based software platform spanning instruments, applications, and shared services across multiple product lines, while embedding AI into how we build and verify that software. That software runs instruments used in over 265,000 labs worldwide, supporting work like R&D, cancer diagnostics, and food and water safety testing.

We’re looking for a Lead Test Framework Architect to own the test frameworks, evaluation methods, and governance that let us ship AI-accelerated software safely in regulated GxP / 21 CFR Part 11 environments. That calls for someone who already has hands‑on, proven depth in Kubernetes test environment architecture, AI and LLM evaluation frameworks, and regulated software validation.

AI accelerates software delivery, but that acceleration exposes weaknesses downstream without a strong automated‑testing control system. You'll build that control system and lead the organization in adopting it.

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 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 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.

Qualifications

Required

• 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 (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: ephemer space 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.

Preferred

• Experience 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 62304 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

• An independent job in collaboration with good colleagues, in a growth-orientated organization
• A true commitment to work/life balance
• An opportunity to learn and grow
• Agilent Result Bonus, Stock Purchase Plan, Life Insurance, Pension, Healthcare, Employee Assistance Program, Holiday, Company activities

Additional Details

This 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: https://careers.agilent.com/locations

Agilent 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 Time

Shift:

Day

Duration:

No End Date

Job Function:

R&D