Senior QA Engineer; AI Systems & Automation

Overview

Essential Software Inc. is a trusted partner to federal agencies, including the National Cancer Institute (NCI), delivering secure, cloud-based platforms that support large-scale cancer data and biomedical research.

As a Senior QA Engineer (AI Systems & Automation), you will lead quality strategy and test automation for critical data platforms and AI‑powered experiences. You will ensure both traditional software and AI/agentic systems are reliable, explainable, and safe in a federal, mission‑driven environment.

• Own end‑to‑end quality for complex web, API, data, and AI/ML‑powered features
• Design AI‑aware test strategies and automation that leverage GenAI and agentic frameworks
• Mentor QA engineers and collaborate closely with cross‑functional teams and government partners
• Develop and maintain test plans, test cases, traceability, and test data for product and AI features
• Execute manual and automated tests for web applications, RESTful APIs, data workflows, and AI/ML features
• Own automated regression suites, release readiness criteria, and provide clear go / no‑go quality signals
• Participate in agile ceremonies, validate end‑to‑end functionality, and ensure user stories (including AI features) meet acceptance criteria
• Manage the full defect lifecycle, including triage, prioritization, root cause analysis, and verification of fixes
• Maintain QA documentation, runbooks, and quality dashboards
• Design and execute test strategies for AI/LLM‑powered capabilities, including virtual agents, chatbots, copilots, and RAG‑based systems
• Use LLM‑powered tools (e.g., ChatGPT, Claude, Copilot) to accelerate test design, data generation, exploratory testing, and script authoring
• Build and refine QA‑focused AI agents that can:
  • Scrape UI and verify DOM structures
  • Validate data against backend or ground‑truth sources
  • Auto‑generate and maintain test scripts
  • Run self‑correcting / autonomous test flows
• Evaluate and integrate agentic frameworks (e.g., OpenAI Assistants API, AWS Bedrock Agents, Lang Graph, MCP) into QA workflows
• Define and monitor AI‑specific quality metrics (accuracy vs. ground truth, hallucination and error rates, safety / policy adherence)
• Ensure AI and virtual agent experiences are accurate, consistent, and high quality in a federal context
• Plan and execute performance, load, and scalability testing (e.g., JMeter or equivalent)
• Validate data integrity and transformation quality across complex biomedical data pipelines and AI‑enhanced workflows
• Partner with engineers and data scientists to ensure AI/ML models and integrations are testable, observable, and measurable post‑deployment
• Mentor QA team members in both traditional and AI‑augmented QA practices
• Collaborate with development, Dev Ops, product, UX, and data teams to improve testability, shift‑left quality, and increase automated coverage
• Integrate automation into CI/CD (e.g., Git Hub Actions, Jenkins, Azure Dev Ops, Git Lab CI), monitor test health and flakiness, and address coverage gaps
• Communicate quality risks, trends, and mitigation plans to technical and non‑technical stakeholders, including government partners

• Bachelor’s degree in computer science, Information Technology, Engineering, or related field
• 5+ years of software QA experience (manual and automation) in production environments
• 2+ years providing technical or process leadership (e.g., lead QA, primary product QA owner, mentor, or manager)
• Strong experience with UI automation tools (Selenium Web Driver, Playwright, or Cypress)
• Experience testing RESTful APIs and microservices architectures
• Hands‑on experience integrating automated tests into CI/CD pipelines (Git Hub Actions, Jenkins, Azure Dev Ops, or Git Lab CI)
• Professional proficiency in Python or JavaScript for test automation
• Hands‑on use of GenAI tools (e.g., ChatGPT, Claude, Copilot) for QA tasks such as test‑case generation, data creation, and exploratory testing
• Understanding of AI/agentic concepts:
  • Tool‑calling / function invocation
  • Multi‑step / chain‑of‑thought workflows
  • Autonomous / self‑healing test flows
  • AI‑driven data comparison and validation
• Experience with performance / load testing (e.g., JMeter or…