# Data scientist - Agentic AI **Company**: Hewlett Packard Enterprise **Location**: San Jose, California **Work arrangement**: onsite **Experience**: senior **Job type**: full-time **Category**: Data Science **Industry**: Technology **Apply**: https://hpe.wd5.myworkdayjobs.com/en-US/Jobsathpe/job/San-Jose-California-United-States-of-America/Data-scientist---Agentic-AI_1208011-2?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_658fcdd0-cab ## Description We are seeking a Data Scientist – Agentic AI to build and operationalize core agentic workflows that power Marvis, Juniper's next-generation AI assistant for network operations. 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. **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:** - 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). - 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). - Kubernetes: deploying, scaling, and managing workloads (Deployments, Services, ConfigMaps, Secrets, health probes). - CI/CD pipelines for automated build, test, and deploy (Jenkins, GitHub Actions, ArgoCD, or similar). - Container image management: building, tagging, versioning via Docker; familiarity with a container registry (ECR, GCR). - Backend service development: FastAPI or equivalent; REST/GraphQL API design. - Observability for AI systems: experience with tracing, monitoring, and logging tools (LangFuse, Prometheus, or equivalent). ## Skills ### Required - LangGraph - MCP - LLM API integration - RAG pipeline design - Kubernetes - CI/CD pipelines - Container image management - Backend service development - Observability for AI systems ### Nice to have - Experience with agent memory systems - Familiarity with sandboxed code execution environments - Networking domain knowledge - Experience with AWS Bedrock, OpenSearch Serverless, or similar managed AI/ML services --- Source: [Apply at hpe.wd5.myworkdayjobs.com](https://hpe.wd5.myworkdayjobs.com/en-US/Jobsathpe/job/San-Jose-California-United-States-of-America/Data-scientist---Agentic-AI_1208011-2?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)