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# Lead Agentic AI Offensive Security Engineer

- **Company:** Hewlett-Packard Enterprise
- **Location:** San Jose, TX, United States
- **Experience:** Expert
- **Salary:** $183,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Cyber Security, Continuous Integration, Information Leak Prevention, Distributed Systems, Memory Management, Fault Tolerance, Internet Security, Intrusion Detection and Prevention, Python (Programming Language), Open Web Application Security, Regression Testing, Software Safety, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Concurrency, Prompt Engineering, Mitre Att&ck, Agentic-AI, Event Driven Architecture, Kubernetes, AutoGen, Claude, Agent SDK, Semantic Kernel, Grpc, Docker, Microservices, Agent2Agent Protocol
- **Published:** November 7, 2026
- **Apply:** https://www.careerbuilder.com/job-details/lead-agentic-ai-offensive-security-engineer-san-jose-tx--ee932a50-bd50-4715-bafd-b5dce5e796a1

## About the Role

Artificial Intelligence (AI), Artificial Intelligence (AI), Hewlett-Packard Product Family, Analysis Skills, Cloud Computing, Internet Security, Technical Support, Production Systems, Memory Management, Memory Hardware, * Candidates should demonstrate deep expertise in at least three areas, with Agentic Systems & Harness Engineering required:

 + System Design & Architecture
 + Agentic Systems & Harness Engineering
 + Model-Agnostic AI & Context Engineering
 + Platform Engineering & Production Reliability
 + Evaluation, Security & AI Safety
 * Experience with cybersecurity automation, security operations, offensive security, adversary simulation, reusable agent platforms, open-weight inference, or frameworks such as OWASP Top 10 for LLM Applications, MITRE ATLAS, MITRE ATT&CK, and NIST AI RMF is preferred.
 * 10+ years of experience in software, platform, distributed-systems, or AI engineering, with demonstrated technical leadership on production systems.
 * Proven experience architecting and operating production LLM-powered or agentic systems across their full lifecycle.
 * Deep expertise in distributed systems, microservices, APIs, event-driven architectures, workflow and state management, scalability, concurrency, and fault tolerance.
 * Strong hands-on experience with agent orchestration, planning, tool calling, delegation, memory, and long-running workflows using LangGraph, Claude Agent SDK, Semantic Kernel, AutoGen, or equivalent.
 * Experience with foundation-model APIs and open-weight or self-hosted models, structured generation, and function calling.
 * Expert Python and backend engineering skills, including REST/gRPC, messaging, Docker, Kubernetes, CI/CD, and cloud infrastructure.
 * Practical experience with agent evaluation, trajectory and regression testing, AI security, and production reliability.

## Description

This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office., HPE's Cybersecurity team is seeking a Lead Agentic AI Engineer to design, enhance and support the technical direction of our AI-Enabled Offensive security organization. Using your expertise in offensive security, you will architect and deliver secure, reliable, production-grade agentic AI platforms. These platforms will accelerate and scale cybersecurity triage, investigation, detection engineering, response, and other complex workflows.

Primary Responsibilities

 * Own the reference architecture and engineering standards for production agentic AI systems.
 * Design reusable agent execution harnesses that separate AI reasoning and planning from deterministic execution.
 * Lead multi-agent workflows, state management, memory, tool calling, human-in-the-loop controls, and long-running processes.
 * Define model-agnostic interfaces supporting commercial, cloud-hosted, and open-weight models, including routing, fallback, and workload-based selection.
 * Architect retrieval, context, and memory services using technologies such as RAG, hybrid search, and vector databases.
 * Establish strategies for MCP servers, A2A protocols, APIs, tool registries, and enterprise integrations.
 * Define evaluation, observability, reliability, and security practices, including least privilege, sandboxing, authorization, secrets management, guardrails, auditability, and defenses against prompt injection, tool abuse, and data leakage.
 * Lead cross-functional architecture decisions, mentor engineers, and communicate technical tradeoffs to senior leadership.