Principal Agentic AI Security Engineer

Hewlett Packard Enterprise

. Design and develop scalable architectures for production-grade agentic AI systems .

Posted 11/5/2026full-timeUnited StatesLead💰 $126,500 - $290,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and developing scalable architectures for agentic AI systems, with a strong focus on backend services, distributed systems, and security engineering principles. Proficient in implementing observability and evaluation capabilities for production-grade software systems.

Highest-signal resume keywords

Agentic AI System DevelopmentDistributed Systems EngineeringSecurity Engineering PrinciplesBackend Software EngineeringMicroservices Architecture

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Hard Skills

PythonREST/gRPC APIsDockerKubernetesCI/CDMulti-Agent ArchitecturesModel-Agnostic InterfacesAsynchronous ServicesVector DatabasesObservability

Soft Skills

Clear CommunicationTechnical Tradeoff Analysis

Tools & Technologies

LangGraphClaude Agent SDKSemantic KernelAutoGenMCP Servers

Industry Keywords

AI EngineeringProduction Software SystemsAgent OrchestrationState ManagementTool Integration

Tech Stack

Tools & technologies

CloudDistributed SystemsDockerGRPCKubernetesMicroservicesPython

About the role

Key responsibilities & impact

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 separating 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

Requirements

What you’ll need

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
Deep expertise in at least two of five technical concentration areas and strong working knowledge across the remaining areas
Knowledge of distributed systems, microservices, APIs, event-driven and asynchronous architectures
Knowledge of workflow, state management, concurrency, scalability, and fault tolerance
Knowledge of agent orchestration, multi-agent architectures, planning, tool calling, delegation, state, memory, and long-running workflows
Experience with MCP, A2A, tool registries, skills, reusable agent capabilities, and execution harnesses
Experience with frameworks such as LangGraph, Claude Agent SDK, Semantic Kernel, AutoGen, or equivalent
Knowledge of foundation model APIs, open-weight/self-hosted models, model abstraction, routing, fallback, benchmarking, and workload-based model selection
Knowledge of structured generation, tool/function calling, RAG, hybrid retrieval, embeddings, re-ranking, vector databases, and context management
Strong Python and backend software engineering
Experience with REST/gRPC APIs, asynchronous services, messaging, and distributed processing
Experience with Docker, Kubernetes, CI/CD, and cloud infrastructure
Experience with 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
Knowledge of AI security, prompt injection, tool abuse, data leakage protections, least privilege, identity, authorization, sandboxing, policy enforcement, guardrails, human approval gates, monitoring, traceability, and auditability
Ability to communicate system architecture, technical tradeoffs, and design decisions clearly

Benefits

Comp & perks

Comprehensive benefits supporting physical, financial, and emotional wellbeing
Personal and professional development programs
Flexible work and personal needs management
Inclusive and accessible workplace
Reasonable accommodation during the application or interview process
Variable incentives may also be offered