Head of AI Quality, Assurance and Evaluation

• Experience building or operating ML/LLM evaluation infrastructure in production, not only authoring policy.
• Working knowledge of regulated computerized systems; GxP, Part 11, or equivalent validation experience strongly preferred.
• The credibility to be trusted by both engineers and compliance officers, and the translation skill to keep them out of each other's way.
• Curiosity about AI, its potential and its pitfalls. The field moves monthly, and the people who thrive here are genuinely curious about both sides of it: what these systems can newly do, and where they fail, mislead, or quietly degrade. We want people who read the failure analyses as eagerly as the launch posts, who experiment on their own initiative, and who hold excitement and skepticism at the same time without letting either one win permanently.
• Lifelong learners. Whatever expertise a candidate arrives with will be partially obsolete within a year, and that is not a defect of the candidate; it is the condition of the field. We hire people who have reinvented their toolkit before and expect to do it again, who learn in public, and who treat being wrong as information rather than injury. A history of deliberate self-reinvention counts for more than any single credential.
• Excellent communication and the ability to influence. Nothing in this organization ships by authority alone. Every role here persuades: domain experts to engage, stewards to share what they know, sponsors to stay honest about value, and functions like Legal, Quality, and Security to move from gatekeeping to partnership. We look for people who write and speak clearly, who adapt their register from bench scientist to Board, and who change minds through credibility and clarity rather than escalation
• Bachelor's or Master's Degree or equivalent.
• Post-graduate, certification, and/or license may be required.
• Typically, at least 10+ years relevant experience for entry to this level.

Agilent inspires and supports discoveries that advance the quality of life by providing life science, diagnostic, and applied market laboratories worldwide with instruments, services, consumables, applications, and both measurement and asset management expertise.

Owns trustworthy deployment of AI in the enterprise. Design-in risk management, evaluation, compliance, and other required constraints into the Enterprise AI solutions development and deployment stack.

Ensure the required technological capabilities across four pillars: Data Substrate (lineage at ingest), Data Plane (semantic grounding), Agentic plane (evaluation and observability), and Activation (risk-tiered human-in-the-loop).

This role exists to make safe deployment fast by leveraging technology to enable quality, regulatory, risk, and other guardrails. For example, leveraging Eval gates to replace manual reviews; auditability instead of after-the-fact forensics.

• Responsible for developing and operationalizing responsible and trustworthy AI practices by designing and implementing risk-based governance frameworks, evaluation methodologies, and compliance controls that enable the safe, scalable, and efficient deployment of AI models, agents, and applications.

• Conducts AI risk, bias, transparency, privacy, and compliance assessments; establishes audit-ready evaluation frameworks, monitoring, and observability standards; and translates legal, quality, security, privacy, and regulatory requirements into measurable and testable controls throughout the AI lifecycle.

• Collaborates closely with cross-functional teams, including engineering, data science, product, legal, quality, and compliance stakeholders, to embed responsible AI principles into development workflows, ensure audit readiness, and mitigate ethical and operational risks.

• Ensure technical architecture fit for Audit and policy via Eval suites and observability, benchmark sets, regression gates for promotion, and production observability for deployed agents, built on the shared eval harness operated with Harness Engineering.

• Develops policies, standards, documentation, and training programs to promote ethical AI awareness, supports governance reviews and technical discussions, and drives continuous improvement of AI deployment practices to deliver trustworthy, compliant, and business-enabling AI solutions.

Administration

10% of the Time

Day

No End Date