Data scientist - Agentic AI

About this 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 pipelines, container image management, health probes, and resource optimization.

• Own observability for agentic workflows — implementing tracing, logging, cost tracking, and performance monitoring to ensure reliability of non-deterministic systems in production.

Education and Experience Required: • Master's or PhD degree in computer science, data science, mathematics, statistics, or a closely related quantitative discipline.

• Typically, 4–6 years of experience building production ML/AI systems, with at least 1–2 years of hands-on work with generative AI and LLM-based applications.

Knowledge and Skills: Agentic AI & GenAI (Required): • Production experience with agentic orchestration frameworks: LangGraph (strongly preferred), LangChain, Claude Agent SDK, or equivalent — beyond prototypes.

• Solid understanding of agentic design patterns: ReACT loops, tool/function calling, dynamic tool binding, skill-based execution, multi-step planning, and self-correction.

• Hands-on experience with MCP (Model Context Protocol) or equivalent tool-serving protocols: tool schema design, server implementation, registry management.

• LLM API integration at scale: prompt engineering, structured outputs, streaming, error handling, and cost optimization.

• RAG pipeline design: chunking strategies, re-ranking, hybrid search, vector stores (OpenSearch or equivalent), and relevance optimization.

• Experience building evaluation and testing frameworks for non-deterministic AI systems (offline evals, A/B testing, LLM-as-judge).

Data Science & ML (Required): • Strong foundation in statistical and machine learning techniques — anomaly detection, time-series analysis, clustering, causal inference, or related methods.

• Applied ML intuition: knowing when to use retrieval vs. fine-tuning, prompt engineering vs. structured generation, and how to debug model behavior in production.

• Proficient Python developer with experience in production codebases (not just notebooks).

Infrastructure & Production Systems (Required): • Kubernetes: deploying, scaling, and managing workloads (Deployments, Services, ConfigMaps, Secrets, health probes).

• CI/CD pipelines for automated build, test, and d

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