Principal Agentic AI Security Engineer
Hewlett Packard Enterprise is hiring a Principal Agentic AI Security Engineer. Level rates it ; you can apply on Hewlett Packard Enterprise.
Level's read on this job
- Level rates this cybersecurity role Builds AI ●●●●: the job is building AI systems.
- The posting mentions Claude, LangGraph and AutoGen among its tools and skills.
- Level classes it as a lead role.
- Hewlett Packard Enterprise has 1,700+ other open roles on Level.
- Posted in the last few days.
AI in this role
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 communicate system architecture, technical tradeoffs, and design decisions clearly.
Accessibility
HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form linked here.
Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.
What We Can Offer You:
Health & Wellbeing
We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.
Personal & Professional Development
We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.
Unconditional Inclusion
We are unconditionally inclusive in the way we work and celebrate individual uniqueness. 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.
Let's Stay Connected:
Follow @HPECareers on Instagram to see the latest on people, culture and tech at HPE.
Job:
Information TechnologyJob Level:
TCP_05"The expected salary/wage range for this position is provided below. Actual offer may vary from this range based upon geographic location, work experience, education/training, and/or skill level.
– United States of America: Annual Salary USD 132,500 - 252,500 in Colorado // 143,000 - 290,500 in California // 126,500 - 290,500 in Georgia & North Carolina & Texas
The listed salary range reflects base salary. Variable incentives may also be offered."
Information about employee benefits offered in the US can be found at https://myhperewards.com/main/new-hire-enrollment.html
The estimated job application period closure is February 1 2027; this timeline is provided for transparency and internal planning purposes.HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer. We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together. Please click here: Equal Employment Opportunity.
Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.
HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.
Recruitment Fraud Alert
We have become aware of an increase in fraudulent recruitment activities in which individuals impersonate our company or authorized recruitment agencies to offer fake employment opportunities. These scams may occur through false websites, emails, social media, or chat-based applications and often aim to obtain personal information or money. Please note that Hewlett Packard Enterprise (HPE), its direct and indirect subsidiaries and affiliated companies, and its authorized recruitment agencies/vendors will never charge a candidate a registration fee, hiring fee, or any other fee in connection with its recruitment and hiring process. We also never request personal information such as back account details, Social Security numbers, or national IDs via social media or chat applications.
All legitimate job opportunities will come through official company channels, and candidates are responsible for verifying the credentials of any third party claiming to represent the company. Any reliance on fraudulent communication is at the individual’s own risk, and HPE disclaims legal liability for any resulting damages. If you suspect recruitment fraud, do not share personal information or make any payments and report the incident to your local authorities immediately.
How we rate this
Principal Agentic AI Security Engineer at Hewlett Packard Enterprise rates 97 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in cybersecurity jobs at this level
Pay
- Typical cybersecurity job
- $160k
This posting doesn't list pay. Use the typical cybersecurity figure when pay comes up. See cybersecurity jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide that one model's output is better than another's for a given task?
- How do you think about the risk of an AI system in this kind of role failing silently?
- What's a project where you used Claude hands-on?
- Walk me through how you've used LangGraph in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: RAG, AI Evaluation, AI Safety, Claude, and LangGraph. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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