Technical Support Engineer - AI Lead

You will operate as a recognized authority: driving the strategic agenda for AI programs across divisions, defining organization-wide Responsible AI, evaluation, and model-governance standards, and holding teams accountable to performance and safety requirements throughout the AI lifecycle. You will be the definitive voice on program goals and prioritization across boundaries, orchestrate complex AI initiatives that span organizations, influence engineering and executive stakeholders on architecture and roadmap, and oversee delivery end to end. You will invest heavily in thought leadership, shaping the discipline and mentoring other program managers. Bachelor's Degree in Computer Science, Information Technology (IT), or related field AND 10+ years technical support, technical consulting experience, or information technology experience These requirements include but are not limited to the following specialized security screenings: Citizenship & Citizenship Verification: This position requires verification of U.S. citizenship due to citizenship-based legal restrictions. Specifically, this position supports United States federal, state, and/or local United States government agency customer and is subject to certain citizenship-based restrictions where required or permitted by applicable law. To meet this legal requirement, citizenship will be verified via a valid passport. Bachelor's Degree in Computer Science, Information Technology, or related field AND 15+ years of technical support, technical consulting experience, or information technology experience OR equivalent experience. 15+ years in technical program or product management with a strong record of principal-level AI/ML program ownership. Sustained record of organization-wide strategic impact in AI and industry credibility Proven history of defining AI evaluation and governance frameworks that get adopted broadly. Executive-level stakeholder management and the judgment to set direction under significant ambiguity and technical risk Expert fluency across the LLM and agentic stack, data and evaluation strategy, model-risk and Responsible AI governance, and the cost, latency, and quality economics of production AI systems.