HPE Labs - AI/ML Engineer (Technical Lead)

Hewlett Packard Enterprise•Milpitas, CA

4d•$178,000 - $361,000•Hybrid

About The Position

Hewlett Packard Enterprise’s Office of the CTO and HPE Labs AI Enablement team helps move AI innovation into production by turning promising ideas into dependable, governed, reusable capabilities for HPE business units and customers. The team works between research and real-world application, building AI systems and practices that can be adopted and scaled across the enterprise. The AI/ML Engineer (Technical Lead) independently leads high-impact “lighthouse” AI use cases from problem framing and solution design through production deployment and transition to operational ownership. This hands-on senior role sets technical direction, designs scalable architectures, and partners with stakeholders across functions and business units to deliver measurable value. The technical lead also establishes reusable architectures, patterns, tooling, and standards to make future use cases more effective; provides technical guidance and mentorship; and contributes to the team’s intake, prioritization, governance, and delivery practices. The role is described as onsite, with an expectation of primarily working from an HPE office. The posting also states “Hybrid – 2 days per week in office.”

Requirements

Typically, 10 or more years of relevant experience in applied AI/ML engineering or software engineering, including hands-on delivery of AI/ML systems into production.
Ability to take an AI/ML solution from concept to production independently, including data pipelines, deployment, evaluation, and operationalization.
Experience shipping an LLM or agentic system used by real users, including ownership of evaluation, reliability, and operation after launch.
Strong, current depth in AI and machine learning, including supervised and unsupervised learning, deep learning, natural language processing, large language models, 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, with comfort in cloud-native and/or hybrid deployment environments.
Working knowledge of responsible-AI and governance practices, including evaluation, guardrails, security-by-design, and data privacy.
Bachelor’s, master’s, or PhD 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.
Strong technical depth and strategic vision, including the ability to shape scalable designs, reusable patterns, and best practices.
Excellent stakeholder and communication skills, including translating technical tradeoffs into business terms, aligning diverse groups, and building trust across functions and business units.
Track record of mentoring and developing less-experienced engineers, with an interest in helping others grow.
Strong judgment and problem-solving skills, including assessing which problems matter, when work is production-ready, and when to escalate or ask for help.

Nice To Haves

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

Responsibilities

Lead 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 evaluation, guardrails, monitoring, documentation, and performance metrics to support trusted adoption and continuous improvement based on operational feedback.
Turn delivery work into reference architectures, tooling, and reusable patterns that reduce the cost of future use cases.
Partner 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 system improvements.
Communicate technical tradeoffs and impact to technical and executive stakeholders, secure sponsorship and operational ownership, and drive adoption through credibility and influence.
Provide technical leadership for significant AI initiatives and guide architecture and design reviews across projects.
Promote consistency and engineering best practices, and mentor less-experienced engineers through code reviews, design reviews, pairing, and example.
Contribute to organizational knowledge sharing and cultivate a collaborative, high-standards team culture.
Help shape the team’s intake, prioritization, governance, and delivery practices.