Description
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.
Experience and Skills
- 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.
Benefits
- Comprehensive suite of benefits that supports physical, financial, and emotional wellbeing.
- Investment in career development and growth.
- Unconditional inclusion and celebration of individual uniqueness.
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