VP - AI Engineering - jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a VP - AI Engineering based in the United States.

This executive role offers the opportunity to shape the future of enterprise AI adoption by building the platforms, frameworks, and engineering practices that enable teams to deliver faster and more effectively. The VP - AI Engineering will lead the development of a central AI engineering ecosystem, empowering developers to leverage AI-assisted and agentic technologies at scale. This position combines strategic leadership with hands-on technical expertise, driving measurable improvements in software delivery velocity, quality, and innovation. You will oversee AI platforms, evaluation systems, security standards, and developer enablement initiatives across the organization. Working closely with engineering, product, security, and governance teams, you will establish the foundation for responsible AI transformation. This is a high-impact opportunity to lead a critical AI function within a regulated technology environment.

Accountabilities:

The VP - AI Engineering will own the strategy, execution, and operational success of the organization’s AI engineering capabilities, ensuring AI platforms and practices deliver measurable business and engineering outcomes.

• Own the AI engineering platform strategy, including the development of centralized AI infrastructure, agent frameworks, connectors, workflows, and shared capabilities used across engineering teams.
• Establish and deliver against ambitious software delivery acceleration goals by defining baselines, implementing DORA-style metrics, and reporting progress to executive stakeholders.
• Build the AI platform as a scalable product with clear roadmaps, service expectations, adoption metrics, and continuous improvement processes.
• Lead the development of AI-assisted and agentic software development practices, including standards, workflows, guardrails, and developer enablement programs.
• Define architecture standards for AI agents, connectors, model integrations, and reusable engineering capabilities to maximize collaboration and reduce duplication.
• Own evaluation and LLMOps infrastructure, including model lifecycle management, testing frameworks, regression evaluation, quality controls, and production readiness standards.
• Ensure AI solutions meet enterprise security, compliance, audit, data governance, and risk management requirements.
• Develop adoption strategies through onboarding programs, enablement initiatives, and change management efforts that drive measurable organizational impact.
• Build and lead a high-performing AI engineering organization focused on accountability, innovation, operational excellence, and continuous learning.
• Partner with executive leaders and cross-functional teams to align AI initiative