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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - Agentic AIThe Data Scientist - **Company:** Hewlett-Packard Enterprise - **Location:** San Jose, CA, United States - **Salary:** $155,500.0 - $315,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Automation of Tests, Cloud Computing, Cluster Analysis, Continuous Delivery, Continuous Integration, Data Normalization, Data Visualization, Database Design, Software Debugging, Software Design Patterns, Github, Image Management, Information Retrieval, Systems Analysis, Python (Programming Language), Network Troubleshooting, Machine Learning, Network Architecture, Network Administration, Octopus Deploy, Performance Tuning, Prometheus, Azure Machine Learning, Search Technologies, Service Development Studio, Software Engineering, Data Streaming, Management of Software Versions, Data Logging, Scripting, Computer Network Operations, ReactJS, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Backend, Juniper, Fastapi, Kubernetes, Information Technology, Performance Monitor, Codebase, Graphql, Data Management, Virtual Agents, Api Design, Restful APIs, Data Pipelines, Serverless Computing, Docker, Jenkins - **Published:** September 30, 2026 - **Apply:** https://www.careerbuilder.com/job-details/data-scientist-agentic-ai-san-jose-ca--cf4f8f98-e425-4019-aa15-fd22b32aa1dd ## About the Role * 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 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). Additional Preferred Skills: * Experience with agent memory systems (e.g., LangMem, custom memory architectures). * Familiarity with sandboxed code execution environments (E2B, Firecracker, or similar). * Networking domain knowledge (wireless/wired diagnostics, network troubleshooting) is a strong plus but not required. * Experience with AWS Bedrock, OpenSearch Serverless, or similar managed AI/ML services. * Great written and verbal communication skills; ability to articulate technical designs to senior leadership., A/B Testing, Amazon Web Services (AWS), Analysis Skills, Application Programming Interface (API), Artificial Intelligence (AI), Cloud Computing, Communication Skills, Computer Science, Continuous Deployment/Delivery, Continuous Integration, Cost Control, Cross-Functional, Data Analysis, Data Collection, Data Management, Data Science, Data Visualization, Database Design, Debugging Skills, Design Patterns Programming Methodologies, Docker, Employee Benefits, Error Handling, GitHub, GraphQL, Hewlett-Packard Product Family, Identify Issues, Image Management, Jenkins, Juniper Networks Product Family, Leadership, Legal, MCP - Microsoft Certified Professional, Machine Learning, Mathematics, Memory Hardware, Mentoring, Network Administration/Management, Network Architecture/Engineering, Performance Analysis, Presentation/Verbal Skills, Problem Solving Skills, Process Improvement, Production Systems, Python Programming/Scripting Language, Quality Management, Quality Metrics, REST (Representational State Transfer), Recruiting/Staffing Agency, Risk, Scripting (Scripting Languages), Social Media, Software Engineering, Statistics, Systems Administration/Management, Systems Analysis, Team Lead/Manager, Technical/Engineering Design, Test Automation, Test Harness, Testing, Time Series Analysis, Wireless Communications, Writing Skills ## Description Data Scientist - Agentic AIThe 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., All legitimate job opportunities will come through official company channels, and candidates are responsible for verifying the credentials of any third party claiming to represent the company. Any reliance on fraudulent communication is at the individual's own risk, and HPE disclaims legal liability for any resulting damages. If you suspect recruitment fraud, do not share personal information or make any payments and report the incident to your local authorities immediately. ## Related Videos - [GitLab CI pipelines for a whole company](https://www.wearedevelopers.com/videos/143-gitlab-ci-pipelines-for-a-whole-company) - [The State of GenAI & Machine Learning in 2025](https://www.wearedevelopers.com/videos/1383-the-state-of-genai-machine-learning-in-2025) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [Our GitOps approach for deploying an Identity Provider and an API Gateway in a SaaS company](https://www.wearedevelopers.com/videos/776-our-gitops-approach-for-deploying-an-identity-provider-and-an-api-gateway-in-a-saas-company) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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