Principal Agentic AI Security Engineer

Hewlett Packard Enterprise • San Jose, TX

1d • $126,500 - $290,500 • Hybrid

About The Position

The Agentic AI Engineer designs, builds, and operates production-grade agentic systems that combine 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, allowing solutions to use commercial, cloud-hosted, or open-weight models according to workload requirements.

The successful candidate has deep expertise in at least two of five technical concentration areas and strong working knowledge across the others: 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. The role is hybrid, with an average requirement of two days per week in 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 experience in software engineering, platform engineering, distributed systems, AI engineering, or a related field.
• Demonstrated experience designing and delivering production software systems.
• Deep expertise in at least two of the five technical concentration areas and strong working knowledge across the remaining areas.
• Knowledge of distributed systems, microservices, APIs, event-driven and asynchronous architectures, workflow and state management, concurrency, scalability, fault tolerance, security boundaries, resiliency, and failure recovery.
• Knowledge of agent orchestration, multi-agent architectures, planning, tool calling, delegation, state, memory, long-running workflows, MCP, A2A, tool registries, skills, reusable agent capabilities, execution harnesses, sandboxed execution, and human-in-the-loop patterns.
• Knowledge of foundation-model APIs and open-weight or self-hosted models; model abstraction, routing, fallback, benchmarking, workload-based model selection, structured generation, tool/function calling, RAG, hybrid retrieval, embeddings, re-ranking, vector databases, context management, and model tradeoffs.
• Strong Python and backend software engineering skills, including REST/gRPC APIs, asynchronous services, messaging, and distributed processing.
• Knowledge of Docker, Kubernetes, CI/CD, cloud infrastructure, logging, metrics, tracing, observability, production troubleshooting, performance, scalability, reliability, and resource optimization.
• Knowledge of agent and model evaluation, regression testing, task-success measurement, tool-use and trajectory evaluation, AI security, prompt-injection and tool-abuse prevention, data-leakage protections, least privilege, 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 that support 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 for team members and their loved ones.
• Career development programs to help employees pursue career goals, become subject-matter experts, or apply their skills in another division.
• Flexibility to manage work and personal needs.

What This Job Offers

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