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Hewlett Packard Enterprise

Builds servers, networking and private cloud

Principal Engineer - AI Architect

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • A bachelor's or master's degree in computer science, engineering, data science, machine learning, artificial intelligence, or a closely related quantitative discipline is required.
  • Typically, 10 or more years of experience are required.
  • Good networking knowledge is expected.
  • Knowledge of Python, Java, Node.js, API design, and microservices is required.
  • Strong knowledge of AI/ML algorithms, large language models, retrieval-augmented generation, and agent-based systems is required.
  • Proficiency in Python and API integration is required.
  • Knowledge of model selection and natural language processing frameworks such as TensorFlow, PyTorch, and Hugging Face is required.
  • Experience with MLOps, containerization using Docker or Kubernetes, and cloud platforms such as AWS, Azure, or GCP is required.
  • Experience with system design for distributed AI applications is required.
  • Knowledge of data architecture, including ETL, feature stores, and vector databases, is required.
  • Knowledge of security and compliance for AI systems is required.
  • Strong communication skills are necessary to collaborate with cross-functional teams, explain complex AI concepts to non-technical stakeholders, and provide technical leadership and mentorship to junior engineers.

Responsibilities

  • Design end-to-end AI systems covering data ingestion, model deployment, and user interfaces.
  • Collaborate with AI/ML engineers, data engineers, and MLOps teams to ensure production readiness.
  • Define an AI roadmap aligned with organizational goals.
  • Identify high-impact AI use cases and assess return on investment.
  • Engage stakeholders early to assess feasibility and governance.
  • Design AI system architecture, including data pipelines, model lifecycle, and orchestration.
  • Integrate AI with enterprise applications and internal systems.
  • Ensure scalability, fault tolerance, and cost efficiency.
  • Select appropriate machine learning and deep learning models and frameworks, including TensorFlow and PyTorch.
  • Define versioning, fine-tuning, and deployment strategies.
  • Implement continuous integration and continuous delivery for machine learning pipelines and monitoring.
  • Enforce data privacy, ethical AI practices, and audit trails.
  • Implement guardrails for large language models and prompt safety.
  • Collaborate with security teams on risk mitigation.
  • Mentor engineers and data scientists.
  • Translate business requirements into technical tasks.
  • Drive collaboration across product, data, and operations teams.

Desired Qualifications

  • Knowledge of cloud platforms and distributed systems, including AWS, Azure, and GCP, and container orchestration.

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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