Principal Agentic AI Offensive Security Engineer

Principal Agentic AI Security Engineer

This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office.

Who We Are

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

Job Description

The Agentic AI Engineer is responsible for designing, building, and operating 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 the engineering of secure and reliable platforms for complex multi-step workflows.

Engineers are expected to design systems where models, frameworks, and inference platforms are replaceable components, allowing solutions to leverage commercial, cloud-hosted, or open-weight models based on workload requirements.

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.

Education and Experience Required

• 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.
• Hands-on experience developing LLM-powered or agentic systems strongly preferred.

Knowledge and Skills Agentic AI engineering requires knowledge across multiple technical disciplines. Successful candidates should demonstrate deep expertise in at least two of the five concentration areas below and strong working knowledge across the remaining areas.

1. System Design & Architecture

• Distributed systems, microservices, APIs, event-driven and asynchronous architectures
• Workflow, state management, concurrency, scalability, and fault tolerance
• System boundaries, security boundaries, resiliency, and failure recovery
• Designing architectures combining deterministic and non-deterministic components

2. Agentic Systems & Harness Engineering

• Agent orchestration and multi-agent architectures
• Planning, tool calling, delegation, state, memory, and long-running workflows
• MCP, A2A, tool registries, skills, and reusable agent capabilities
• Execution harnesses, sandboxed execution, and human-in-the-loop patterns
• Frameworks such as LangGraph, Claude Agent SDK, Semantic Kernel, AutoGen, or equivalent

3. Model-Agnostic AI & Context Engineering

• Foundation model APIs and open-weight/self-hosted models
• Model abstraction, routing, fallback, benchmarking, and workload-based model selection
• Structured generation and tool/function calling
• RAG, hybrid retrieval, embeddings, re-ranking, vector databases, and context management
• Understanding model capability, latency, cost, security, and operational tradeoffs

4. Platform Engineering & Production Reliability

• Strong Python and backend software engineering
• REST/gRPC APIs, asynchronous services, messaging, and distributed processing
• Docker, Kubernetes, CI/CD, and cloud infrastructure
• Logging, metrics, tracing, observability, and production troubleshooting
• Performance, scalability, reliability, and resource optimization

5. Evaluation, Security & AI Safety

• Agent and model evaluation, regression testing, and task-success measurement
• Tool-use and trajectory evaluation
• AI security, prompt injection, tool abuse, and data leakage protections
• Least privilege, identity, authorization, sandboxing, and policy enforcement
• Guardrails, human approval gates, monitoring, traceability, and auditability

Additional Preferred Skills

• 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 commu