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Hewlett Packard Enterprise

Builds servers, networking and private cloud

AI/ML Engineer Technical Lead

Applied Machine LearningPosted on Nov 9
Full-timeMilpitas, CA, USA$178k - $361k /yr

About the Job

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 take an AI/ML solution from concept to production independently, including 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 and comfort with cloud-native and/or hybrid deployment environments.
  • Working knowledge of responsible-AI and governance practices, including evaluation, guardrails, security-by-design, data privacy, and building these practices into systems.
  • Bachelor's, Master's, or PhD in computer science, computer engineering, data science, or a related field, or equivalent practical experience.
  • Proven ability to lead technical work and influence across teams without formal authority.
  • Ability to shape scalable designs, reusable patterns, and best practices using technical depth and strategic vision.
  • Ability to translate technical tradeoffs into business terms, align diverse groups, and build trust across functions and business units.
  • Track record of mentoring and developing less-experienced engineers.
  • Strong judgment and problem-solving, including deciding which problems matter, when a solution is production-ready, and when to escalate or ask.

Responsibilities

  • Independently own selected high-impact AI use cases from problem framing and solution design through production deployment and transition to operational ownership, applying LLM, agentic, and applied AI approaches where appropriate.
  • Design AI system architectures for scalability, performance, reliability, security, and supportability.
  • Establish evaluation, guardrails, monitoring, documentation, and performance metrics for trusted adoption in real workflows, and continuously improve systems based on operational feedback.
  • Turn deliveries into reference architectures, tooling, and repeatable patterns that lower the cost of subsequent use cases.
  • Work 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.
  • Provide technical guidance, communicate tradeoffs and impact to technical and executive stakeholders, secure sponsorship and operational ownership, and drive adoption through credibility and influence.
  • 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 organizational knowledge sharing and cultivate a collaborative, high-standards team culture.
  • Contribute to the team's intake, prioritization, governance, and delivery practices.

About the Company

Hewlett Packard Enterprise

Builds servers, networking and private cloud

Hewlett Packard Enterprise sells servers, storage, networking and hybrid cloud software to large companies, governments and service providers. Its GreenLake platform lets customers run on-premises hardware as a metered cloud service, and its Cray supercomputers run national lab and AI training workloads. The networking business, built on Aruba and Juniper Networks, supplies campus, data-center and routing equipment. Revenue comes from hardware sales, multiyear support contracts, financing and subscriptions. The company traces its roots to Hewlett-Packard, founded in 1939, became a separate public company in 2015 and is headquartered in Houston, Texas.

Company size
10,001+
Company stage
IPO
Headquarters
Houston, TX
Founded
1939

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