HPE Labs - AI/ML Engineer (Technical Lead)

HPE Labs - AI/ML Engineer (Technical Lead) This role has been designed as ‘’Onsite’ with an expectation that you will primarily work 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: Who We Are Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. Within HPE’s Office of the CTO and HPE Labs, the AI Enablement team builds on HPE’s established AI research, engineering, and market expertise to accelerate the transition of innovation into production by turning promising ideas into dependable, governed, reusable capabilities that HPE’s business units and customers can adopt and build on. We sit at the seam between invention and impact. We don’t just prototype; we make AI real, repeatable, and trusted at enterprise scale. The team combines HPE’s deep technical expertise with a collaborative, high-standards, and intellectually honest culture focused on solving hard problems. If you want to shape how a world-class technology company operationalizes AI, this is the team. Role Overview We’re hiring a technical lead for the AI Enablement team. As a tech lead, you’ll independently drive high-impact “ lighthouse ” AI use cases from concept through production: owning the technical direction, partnering across functions and business units to deliver value, and establishing scalable architectures, patterns , and standards that make each subsequent use case faster and more effective . This is a hands-on senior role for someone who's technically excellent and genuinely good with people: equally comfortable in an architecture deep-dive, a stakeholder negotiation, and a mentoring conversation. You'll have unusual scope to shape both the work and the team from day one. Key Responsibilities Lead lighthouse use cases end-to-end. Independently own selected high-impact AI use cases from problem framing and solution design through production deployment and the transition to operational ownership, bringing LLM, agentic, and applied AI approaches to bear where they fit. Build for production and measurable impact. Design the architecture of AI systems for scalability, performance, reliability, security, and supportability. Establish evaluation, guardrails, monitoring, documentation, and performance metrics that enable trusted adoption in real workflows, and continuously improve the systems based on operational feedback. Create reusable patterns. Turn each delivery into reference architectures, tooling, and repeatable patterns that lower the cost of the next use case, so the team scales through leverage, not headcount. Partner across the organization. Work closely with stakeholders across business units, product, engineering, data, security, and HPE Labs to translate customer requirements, business needs, and industry trends into AI products, solutions, and systems improvements. Provide technical guidance, communicate tradeoffs and impact to technical and executive stakeholders, secure sponsorship and operational ownership, and drive adoption through credibility and influence. Provide technical leadership and mentorship. Serve as a recognized technical authority for significant AI initiatives, guide architecture and design reviews across projects, promote consistency and engineering best practices, and coach less-experienced engineers through code review, design review, pairing, and example. Contribute to knowledge sharing across the organization and help cultivate a collaborative, high-standards team culture. Help shape how we work. Contribute to the team's intake, prioritization, governance, and delivery practices as we build them, bringing judgment about what's worth doing and how to do it well. Preferred Qualifications Experience & technical skills: Typically, 10+ years of relevant experience in applied AI/ML engineering, or software engineering, including hands-on delivery of AI/ML systems into production. Demonstrated ability to take an AI/ML solution from concept to production independently, not just modeling, but the full lifecycle: data pipelines, deployment, evaluation, and operationalization. Demonstrated experience shipping an LLM or agentic system that real users depend on, with ownership of its evaluation, reliability, and operation after launch. Experience building and scaling enterprise agentic AI solutions across complex, production-grade environments. Strong, current depth across AI and machine learning, including supervised and unsupervised learning, deep learning, natural language processing, large language models, generative AI, agentic systems, and statistical modeling, with practical experience in LLMOps or MLOps, evaluation, and production model lifecycle management. Solid systems and software engineering fundamentals; comfort with cloud-native and/or hybrid deployment environments. Working knowledge of responsible-AI and governance practices: evaluation, guardrails, security-by-design, data privacy, and how to build them in rather than bolt them on. Bachelor's or master’s in computer science, computer engineering, data science, or related field, or equivalent practical experience. Leadership & collaboration: Proven ability to lead technical work and influence across teams without formal authority: earning adoption by being useful and credible, not by mandate. Combines strong technical depth with a clear strategic vision, helping shape scalable designs, reusable patterns, and best practices. Excellent stakeholder and communication skills: able to translate technical tradeoffs into business terms, align diverse groups, and build trust across functions and business units. A track record of mentoring and developing less-experienced engineers; genuine interest in growing others. Strong judgment and problem-solving: knowing which problems matter, when something is production-ready, and when to escalate or ask. Who Thrives Here Success in this role depends as much on how you work as on what you know. The people who thrive here and strengthen the culture we value tend to be: Builders and finishers : energized by shipping real, dependable things, not just prototypes; they care about the unglamorous 80% (integration, evaluation, support) that makes AI work. Intellectually honest : comfortable saying "I don't know," naming risks early, giving and receiving direct feedback, and challenging ideas respectfully. We're building a culture of psychological safety and high standards, and that starts with candor. Collaborative by instinct : they bring people together across boundaries, assume good intent, and would rather make the whole team succeed than be the smartest person in the room. Curious and humble : eager to learn, including from the researchers whose work they help transition and from the business partners whose problems they solve. Growth-minded and generous : they treat setbacks as learning, lift the people around them, and want to help shape a team that's both excellent and a good place to work. Accessibility HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form