Principal Agentic AI Offensive Security Engineer

Hewlett Packard Enterprise • Durham, NC

2d • $126,500 - $290,500 • Hybrid

About The Position

The Agentic AI Engineer designs, builds, and operates production-grade agentic systems combining foundation models, software services, enterprise data, tools, and automation. The role focuses on system architecture, agent orchestration, model-agnostic AI integration, deterministic execution, and secure, reliable platforms for complex multi-step workflows. Models, frameworks, and inference platforms should be replaceable components so solutions can use commercial, cloud-hosted, or open-weight models according to workload requirements.

The role calls for deep expertise in at least two of five areas—system design and architecture; agentic systems and harness engineering; model-agnostic AI and context engineering; platform engineering and production reliability; and evaluation, security, and AI safety—with working knowledge across the remaining areas. The position is hybrid, requiring an average of two days per week at an HPE office.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related technical discipline, or equivalent practical experience.
• Typically 5–8 years of software engineering, platform engineering, distributed systems, AI engineering, or related experience.
• Demonstrated experience designing and delivering production software systems.
• Deep expertise in at least two of the listed concentration areas and strong working knowledge across the remaining areas.
• Strong Python and backend software engineering skills.
• Knowledge of distributed systems, microservices, APIs, event-driven and asynchronous architectures, workflow and state management, concurrency, scalability, fault tolerance, and failure recovery.
• Knowledge of agent orchestration, multi-agent architectures, planning, tool calling, delegation, state, memory, long-running workflows, MCP, A2A, and execution harnesses.
• Knowledge of foundation model APIs, model abstraction and routing, structured generation, tool/function calling, RAG, hybrid retrieval, embeddings, re-ranking, vector databases, and context management.
• Knowledge of REST/gRPC APIs, asynchronous services, messaging, distributed processing, Docker, Kubernetes, CI/CD, cloud infrastructure, observability, and production troubleshooting.
• Knowledge of agent and model evaluation, regression testing, task-success measurement, AI security, prompt injection, tool abuse, data leakage protections, identity, authorization, sandboxing, policy enforcement, guardrails, human approval gates, monitoring, traceability, and auditability.

Nice To Haves

• Hands-on experience developing LLM-powered or agentic systems.
• Experience building reusable agent platforms, harnesses, or tool ecosystems.
• Experience with cybersecurity automation or security-sensitive systems.
• Experience operating open-weight models and commercial foundation-model platforms.
• Experience with enterprise-scale distributed or cloud-native systems.
• Ability to communicate system architecture, technical tradeoffs, and design decisions clearly.

Responsibilities

• Design and develop scalable architectures for production-grade agentic AI systems.
• Build agent orchestration, multi-agent workflows, state management, memory, tool integration, and human-in-the-loop capabilities.
• Design reusable execution harnesses and services that separate AI reasoning and planning from deterministic execution.
• Develop model-agnostic interfaces supporting multiple foundation models, providers, and inference platforms.
• Build and integrate MCP servers, APIs, tool registries, skills, enterprise services, and data sources.
• Develop retrieval and context services, including RAG, hybrid search, vector databases, and agent memory.
• Build backend services supporting asynchronous and distributed agent execution.
• Design evaluation and testing capabilities for agent behavior, tool usage, task completion, security, reliability, and performance.
• Implement observability across agent execution, model interactions, tools, state transitions, and system performance.
• Apply security engineering principles, including least privilege, sandboxing, authorization, secrets management, guardrails, and auditability.
• Deploy and operate agentic services using containers, Kubernetes, CI/CD, and modern cloud-native engineering practices.
• Evaluate emerging models, frameworks, protocols, and technologies while maintaining architectural independence from individual vendors or platforms.

Benefits

• A comprehensive suite of benefits supporting physical, financial, and emotional wellbeing.
• Personal and professional development programs to support career goals.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal