Jobs / United States / Hewlett Packard Enterprise Company
Data scientist - Agentic AI
Hewlett Packard Enterprise Company · 🇺🇸 San Jose, California, United States of America
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 486 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 167 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Hewlett Packard Enterprise Company paid sponsored hires in similar roles3 certified filings for “Senior AI and Machine Learning Engineer” (Data Scientists) in CA: $124k–$228k, median $174k. Most were filed at wage level I (50%) — 1 lottery entry, ≈15% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Hewlett Packard Enterprise Company
The US Department of Labor certified 486 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 167 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Hewlett Packard Enterprise Company →
About the role
Data scientist - Agentic AI 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: Data Scientist – Agentic AI The Data Scientist – Agentic AI builds and operationalizes the core agentic workflows that power Marvis, Juniper's next-generation AI assistant for network operations. Working at the intersection of data science, generative AI, and production engineering, this role is responsible for designing, implementing, and evaluating the reasoning pipelines, tool-calling patterns, skills, and MCP server integrations that enable Marvis to autonomously diagnose, troubleshoot, and resolve complex networking problems. The ideal candidate combines deep hands-on experience with LLM-based agentic frameworks (LangGraph preferred) with the software engineering rigor needed to ship reliable, observable AI systems in a cloud-native environment. Management Level Definition: Contributions impact technical components of products, solutions, or services regularly and sustainably. Applies advanced subject matter knowledge to solve complex business and technical problems and is regarded as a subject matter expert in agentic AI and applied GenAI. Provides expertise and partnership to functional and technical project teams and may participate in cross-functional initiatives. Exercises significant independent judgment to determine best method for achieving objectives. May provide team leadership and mentoring to others. Responsibilities: • Design, implement, and iterate on agentic workflows using LangGraph, including ReACT orchestration loops, dynamic tool selection and binding, multi-step reasoning, and self-correction patterns. • Develop and maintain MCP (Model Context Protocol) servers and skills — defining tool schemas, implementing domain-specific tools, writing skill playbooks (SKILL.md), and managing server lifecycle (versioning, deployment, monitoring). • Integrate and optimize LLM capabilities at production scale, including structured outputs, streaming, function/tool calling, prompt engineering, and robust error handling across agent execution paths. • Build and refine retrieval and memory services for agentic systems, including RAG pipelines, vector-store-backed semantic search, hybrid retrieval, long-term agent memory (semantic, episodic, procedural), and relevance tuning. • Design and execute evaluation frameworks for non-deterministic agentic systems — defining metrics, building test harnesses, running A/B tests on skills and tool configurations, and driving continuous quality improvement. • Collaborate with domain experts (network engineers, product managers) to formalize networking problems as agentic workflows, translating troubleshooting playbooks into skills, tools, and data pipelines. • Develop data analysis and transformation logic that runs in sandboxed execution environments (Code Mode), including multi-tool orchestration scripts, data aggregation, and visualization. • Deploy and operate containerized services in Kubernetes, contributing to CI/CD pipe