HPE Labs - AI/ML Engineer (Technical Lead)

Hewlett Packard Enterprise•Milpitas, CA

5d•$178,000 - $361,000•Onsite

About The Position

Hewlett Packard Enterprise’s Office of the CTO and HPE Labs AI Enablement team helps transition AI innovation into dependable, governed, reusable capabilities that business units and customers can adopt. The team works between research and production, turning promising ideas into trusted enterprise-scale AI systems. The AI/ML Engineer will serve as a technical lead, independently driving high-impact AI use cases from concept through production and operational ownership. The role sets technical direction, partners with stakeholders across functions and business units, and establishes scalable architectures, patterns, and standards. It is a hands-on senior position combining deep technical work with stakeholder collaboration, technical leadership, and mentorship. The position is designed as onsite, with an expectation of primarily working from an HPE office. The stated annual base salary range in California is USD 178,000–361,000. Variable incentives may also be offered.

Requirements

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 independently take AI/ML solutions from concept to production across the full lifecycle, including data pipelines, deployment, evaluation, and operationalization.
Experience shipping an LLM or agentic system used by real users, including ownership of evaluation, reliability, and post-launch operation.
Strong current knowledge of AI and machine learning, including supervised and unsupervised learning, deep learning, NLP, LLMs, generative AI, agentic systems, and statistical modeling.
Practical experience with LLMOps or MLOps, evaluation, and production model lifecycle management.
Solid systems and software engineering fundamentals and comfort with cloud-native and/or hybrid deployment environments.
Working knowledge of responsible AI and governance, including evaluation, guardrails, security-by-design, and data privacy.
Bachelor’s or master’s degree in computer science, computer engineering, data science, or a related field, or equivalent practical experience.
Ability to lead technical work and influence across teams without formal authority.
Ability to shape scalable designs, reusable patterns, and best practices with strong technical depth and strategic vision.
Excellent stakeholder and communication skills, including translating technical tradeoffs into business terms and building trust across functions and business units.
Track record of mentoring and developing less-experienced engineers.
Strong judgment and problem-solving skills, including knowing which problems matter, when a system is production-ready, and when to escalate or ask for help.

Nice To Haves

Experience building and scaling enterprise agentic AI solutions across complex, production-grade environments.

Responsibilities

Own selected high-impact AI use cases end-to-end, from problem framing and solution design through production deployment and transition to operational ownership.
Apply LLM, agentic, and other applied AI approaches where appropriate.
Design AI system architectures for scalability, performance, reliability, security, and supportability.
Establish evaluations, guardrails, monitoring, documentation, and performance metrics; improve systems using operational feedback.
Convert delivered solutions into reference architectures, tooling, and reusable patterns.
Partner with stakeholders across business units, product, engineering, data, security, and HPE Labs to translate requirements, business needs, and industry trends into AI products, solutions, and system 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.
Act as a technical authority for significant AI initiatives; guide architecture and design reviews, promote engineering best practices, and coach less-experienced engineers through reviews, pairing, and example.
Share knowledge across the organization and contribute to a collaborative, high-standards team culture.
Help develop the team's intake, prioritization, governance, and delivery practices.