Senior Software Engineering Manager

Senior Software Engineering Manager (Finance)

Senior Software Engineering Manager

This role has been designed as 'Hybrid' with a requirement 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:

Senior Software Engineering Manager - AI Data Center Networking

Location: Sunnyvale, CA

Lead a geographically distributed team of solutions and test engineers responsible for architecting, validating, and publishing Juniper Validated Designs (JVDs) across AI/ML cluster, data center, DCI, and campus portfolios. The manager owns team delivery, people development, lab and testbed investment, and cross-functional alignment for NPI programs, while remaining technically credible enough to guide architecture decisions and customer escalations.

KEY RESPONSIBILITIES

People Leadership

• Manage, hire, and develop a global team of solutions engineers, including goal setting, performance reviews, career planning, and compensation input.
• Build team autonomy through coaching and mentoring, and develop a succession pipeline of senior and principal engineers.
• Foster a culture of quality ownership, collaboration, and continuous learning across sites and time zones.

Program and Delivery Ownership

• Own the solution validation roadmap and quarterly commitments for NPI programs, from multi-feature testing through scale validation and JVD publication.
• Plan capacity, prioritize work across competing programs, and manage schedule risk and dependencies.
• Define and report team metrics: release quality, defect escape rate, automation coverage, JVD delivery, and escalation turnaround.
• Act as a release quality gate, with authority to hold releases that do not meet customer-readiness criteria.

Technical Strategy

• Set technical direction for AI/ML data center validation: scale-out and scale-up GPU fabrics, RoCEv2, load balancing (DLB, GLB, RLB),