Principal Quality Assurance Engineer – Generative AI & LLM Platforms

Principal Quality Assurance Engineer – Generative AI & LLM Platforms

  

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

Job Description:

   

Job Family Definition:

Designs, develops, troubleshoots and debugs software programs for software enhancements and new products. Develops software including operating systems, compilers, routers, networks, utilities, databases and Internet-related tools. Determines hardware compatibility and/or influences hardware design.

Management Level Definition:

Contributions have visible technical impact on a product or major subcomponent.  Applies in-depth professional knowledge and innovative ideas to solve complex problems. Visible contributions improve time-to-market, achieve cost reductions, or satisfy current and future unmet customer needs. Recognized internal authority on key technology area applying innovative principles and ideas. Provides technical leadership for significant project/program work. Leads or participates in cross-functional initiatives and contributes to mentorship and knowledge sharing across the organization.

Role Overview:

We are seeking a Senior Quality Engineer to own quality strategy and hands-on validation for enterprise products built with Generative AI, large language models, distributed systems, and modern React-based user interfaces.

This role combines strong software testing fundamentals with AI evaluation expertise. The successful candidate will design automated quality frameworks, test deterministic and probabilistic behaviour, uncover complex cross-layer defects, influence architecture for testability, and help engineering teams deliver secure, reliable, accessible, high-performance products at speed.

Responsibilities:

• Define risk-based test strategies, quality gates, release criteria, traceability, and measurable objectives across AI, UI, API, cloud, and network layers.

• Evaluate model quality, groundedness, safety, privacy, robustness, tool use, permissions, failure recovery, latency, and cost.

• Validate React interfaces, web standards, accessibility, security, APIs, asynchronous workflows, and enterprise integrations.

• Test AWS deployments, distributed systems, traffic behavior, scaling, failover, disaster recovery, and adverse network conditions.

• Build reusable Python and pytest frameworks and execute performance, scale, reliability, and end-to-end testing with CI/CD integration.

• Use telemetry, incidents, and user feedback to detect regressions, strengthen coverage, and improve preventive controls.

• Partner across disciplines, use AI-assisted testing responsibly, mentor engineers, and promote shared ownership of quality.

Education and Experience Required:

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related discipline.

• 12+ years’ experience with at least 3 years of experience in a lead role

Knowledge and Skills:

• Quality Engineering: Enterprise test strategy, automation, quality governance

• Automation: Python, pytest, UI/API testing, mocking, diagnostics, CI/CD

• Web Testing: React, responsive design, accessibility, cross-browser, web security,

• API testing: JMeter, SoapUI, Postman

• GenAI Evaluation: LLM testing, groundedness, hallucination, safety, regression, metrics, human review, LangSmith, LangGraph, Langfuse, MCP

• Networking & Protocols: IP clos fabric, EVPN, VXLAN, BGP, MPLS, NETCONF, RESTCONF, gRPC, SNMP, LLDP, traffic simulators such as Ixia, Spirent etc…

• Performance Engineering: Performance, scale, functional, integration, E2E, regression, security, accessibility, reliability, compatibility

• Problem-Solving: Architecture analysis, cross-layer debugging, risk assessment, root-cause communication

• AI quality and observability: Experience with agent evaluation, prompt regression, model comparison, tracing, and production monitoring.

• Performance and resilience: Proficiency in API and browser performance testing, cloud-scale resilience, and chaos testing.

• Security and complex platforms: Knowledge of OWASP risks, threat modelling, adversarial validation, and testing multi-tenant or distributed enterprise architectures.

• AWS and monitoring: Familiarity with AI services, container platforms, and observability tools such as Datadog.

• Credentials and leadership: Relevant networking certifications and demonstrated leadership across automation, cloud quality, performance, security, or responsible AI.

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.

Job:

Engineering

Job Level:

TCP_05

    

    

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.

   

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