Senior Technical Program Manager - AI Engineering Productivity

Senior Technical Program Manager - AI Engineering Productivity

This role has been designed as 'Hybrid' with an expectation that you will work on average 2 days per week from an HPE office.

Who We Are:

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work.

We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today's complex world.

Our culture thrives on finding new and better ways to accelerate what's next.

We know varied backgrounds are valued and succeed here.

We have the flexibility to manage our work and personal needs.

We make bold moves, together, and are a force for good.

If you are looking to stretch and grow your career our culture will embrace you.

Open up opportunities with HPE.

Job Description:

We are seeking an experienced Senior Technical Program Manager - AI Engineering Productivity to drive AI-powered software development productivity initiatives across our engineering organization.

Reporting to the VP of Engineering, this role will focus on accelerating engineering efficiency through adoption of AI-assisted development tools, modern engineering workflows, and developer productivity platforms.

The ideal candidate will partner closely with Development, QA, SRE, and Support teams to improve code delivery velocity, streamline engineering workflows, and drive measurable productivity gains across the software development lifecycle.

This highly visible role will lead cross-functional AI initiatives, drive organization-wide adoption of AI tooling, develop engineering productivity metrics and dashboards, and provide regular executive updates on roadmap progress, adoption trends, and business impact.

Responsibilities


* Lead adoption and enablement programs for AI-assisted development tools including coding assistants, workflow automation, and developer productivity platforms.


* Partner with engineering teams to improve developer efficiency, code delivery velocity, testing workflows, and operational effectiveness.


* Define and track engineering productivity metrics including AI adoption, code acceptance rates, unit test generation, workflow efficiency, and token utilization.


* Drive development of dashboards and reporting systems to measure engineering productivity and AI usage trends.


* Coordinate AI-assisted workflow initiatives including test generation and engineering automation.


* Drive AI-assisted support and incident management workflows, including agent-based Jira enrichment, runbook automation, log diagnostics, and RCA acceleration for customer-reported issues and alerting events.


* Establish and enforce AI tool usage governance, including cost management, token budget policies, usage anomaly detection, and guardrails against underutilization and token abuse across engineering teams.


* Organi...