Lead AI Engineer - Agentic Test Automation

About the role

Fully onsite in McLean, VA (Tysons). Contract position. Salary range: $65–90 per hour. Work hours: 9am–5pm. Education: Bachelor’s degree.

Responsibilities

• Agentic test automation foundation (reusable patterns + reference implementations)
  • Minimum coverage expectations by service/component
  • Test plan templates
• GenAI-assisted reporting and quality insights across microservices
  • Test execution results (Karate/Playwright + CI runs)
  • Release readiness narratives
• “Quality gates” via agents
  • Required fields present (acceptance criteria, testable outcomes, data needs, dependencies)
• Design and implement agentic testing patterns that can be adopted by multiple Underwriting teams (and later other domains).
• Create reference implementations (sample repos / templates) demonstrating:
  • Test generation assistance (from requirements, APIs, contracts, schemas)
  • Test maintenance assistance (auto-updating selectors/contracts, flaky test triage)
  • Failure analysis assistance (root cause suggestions, log correlation, defect drafting)
• Establish a standard architecture for test code organization, tagging, data management, and execution across UI + API + service layers.
• Coverage standards, templates, and governance
  • Define and publish coverage standards (what “good” looks like) including:
    • Test type mix (unit vs API vs UI vs contract vs integration)
    • Risk-based prioritization and traceability to requirements
  • Provide templates usable across teams:
    • Test case/spec templates (Gherkin-style or equivalent)
    • Definition of Ready / Definition of Done quality checklists
• Create a scalable tagging/metadata strategy (e.g., feature, service, risk, priority, data sensitivity) to support reporting and quality gates.
• Build automated reporting that aggregates test + service data across multiple microservices, such as:
  • Service health signals (logs/metrics/traces if available)
  • Defect signals (issue tracker metadata if available)
• Generate GenAI-driven summaries:
  • Failure clustering and trend analysis
  • “What changed?” insights (commit/PR correlation)
• Produce outputs consumable by engineering leadership and teams (dashboards, markdown summaries in PRs, artifacts in CI).
• Build automated review agents that evaluate user stories/requirements for minimum required clarity and data before development/testing starts: