Principal QA Architect - Generative AI & LLM Platforms

Hewlett Packard Enterprise Company in; San Juan (PR); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Health & Wellbeing

Professional Development

Inclusive culture

Job summary

Hewlett Packard Enterprise seeks a Principal Quality Assurance Engineer to own quality strategy and hands-on validation for enterprise Generative AI platforms. You will design automated frameworks, test multi-layer systems, and ensure secure, high-performing software at speed.

The role blends testing fundamentals with AI evaluation, requiring leadership in automation, cross-functional collaboration, and responsible AI practices. Hybrid work with two days in-office per week.

Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or related field.
• 12+ years' experience with at least 3 years in a lead role.

Responsibilities

• Define risk-based test strategies and quality gates across AI, UI, API, cloud, and network layers.
• Evaluate model quality, groundedness, safety, privacy, robustness, and cost.
• Validate React interfaces, accessibility, security, and enterprise integrations.
• Test AWS deployments, distributed systems, traffic behavior, scaling, failover, and disaster recovery.
• Build reusable Python and pytest frameworks for performance, scale, and end-to-end testing with CI/CD.
• Use telemetry and incidents to detect regressions and improve coverage.
• Mentor engineers and promote shared ownership of quality.

Skills

Quality Assurance

Python

pytest

UI testing

API testing

React testing

AI quality

AWS

CI/CD

Education

Bachelor's or Master's in CS/ML/AI

Tools

JMeter

SoapUI

Postman

Job description

Hewlett Packard Enterprise seeks a Principal Quality Assurance Engineer to own quality strategy and hands-on validation for enterprise Generative AI platforms. You will design automated frameworks, test multi-layer systems, and ensure secure, high-performing software at speed.

The role blends testing fundamentals with AI evaluation, requiring leadership in automation, cross-functional collaboration, and responsible AI practices. Hybrid work with two days in-office per week.