Principal Quality Assurance Engineer - Generative AI & LLM Platforms

About the role

Hewlett Packard Enterprise is seeking a Principal Quality Assurance 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. The role combines software testing fundamentals with AI evaluation expertise. It is hybrid, with about two days per week working from an HPE office.

Key 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 run performance, scale, reliability and end-to-end tests integrated with CI/CD.
• Use telemetry, incidents and user feedback to detect regressions and strengthen coverage.
• Partner across disciplines, use AI-assisted testing responsibly, mentor engineers and promote shared ownership of quality.

Required profile

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence or a related discipline.
• 12+ years of experience, including at least 3 years in a lead role.
• Relevant networking certifications and demonstrated leadership across automation, cloud quality, performance, security or responsible AI.

Required skills

• Quality engineering: enterprise test strategy, automation, quality governance
• Python, pytest, UI/API testing, mocking, diagnostics, CI/CD
• React, responsive design, accessibility, cross-browser testing, web security
• JMeter, SoapUI, Postman
• LLM testing, hallucination, safety, metrics, LangSmith, LangGraph, Langfuse, MCP
• Networking protocols (IP Clos fabric, EVPN, VXLAN, BGP, MPLS, NETCONF, RESTCONF, gRPC, SNMP, LLDP) and traffic simulators such as Ixia and Spirent
• Performance, scale, functional, integration, E2E, regression, security, accessibility and reliability testing
• AWS, container platforms, Datadog, OWASP, threat modelling, chaos testing