Senior Quality Engineer – Generative AI & Enterprise Web Platforms

Hewlett Packard Enterprise•San Juan, PR

3d•Hybrid

About The Position

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

Requirements

Bachelor’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related discipline
10 years’ experience with 3 years in a lead role
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 behaviour, 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.
Experience with agent evaluation, prompt regression, model comparison, tracing, and production monitoring.
Proficiency in API and browser performance testing, cloud-scale resilience, and chaos testing.
Knowledge of OWASP risks, threat modelling, adversarial validation, and testing multi-tenant or distributed enterprise architectures.
Familiarity with AI services, container platforms, and observability tools such as Datadog.
Relevant networking certifications and demonstrated leadership across automation, cloud quality, performance, security, or responsible AI.

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 behaviour, 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.