Source description
About the role
Job Title: AI Test Engineer
Location: McLean, VA
Duration: 6+ months contract with possible extension
Job Description:
- Agentic test automation foundation (reusable patterns + reference implementations)
• 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: o Test generation assistance (from requirements, APIs, contracts, schemas) o Test maintenance assistance (auto-updating selectors/contracts, flaky test triage) o 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: o Minimum coverage expectations by service/component
o Test type mix (unit vs API vs UI vs contract vs integration) o Risk-based prioritization and traceability to requirements
• Provide templates usable across teams: o Test plan templates o Test case/spec templates (Gherkin-style or equivalent)
o 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.
- GenAI-assisted reporting and quality insights across microservices
• Build automated reporting that aggregates test + service data across multiple microservices, such as:
o Test execution results (Karate/Playwright + CI runs)
o Service health signals (logs/metrics/traces if available)
o Defect signals (issue tracker metadata if available)
• Generate GenAI-driven summaries:
o Release readiness narratives o Failure clustering and trend analysis
o “What changed?” insights (commit/PR correlation)
• Produce outputs consumable by engineering leadership and teams (dashboards, markdown summaries in PRs, artifacts in CI).
- “Quality gates” via agents
• Build automated review agents that evaluate user stories/requirements for minimum required clarity and data before development/testing starts:
o Required fields present (acceptance criteria, testable outcomes, data needs, dependencies) o Ambiguity detection and missing edge cases
o Data/privacy considerations and environment needs
• Integrate gates into workflow (PR checks, issue templates, GitHub Actions) to reduce churn and rework.
Required Technical Skills (must-have) GenAI / LLM + agentic development
• Hands-on experience building LLM-powered agents (tool-using, multi-step reasoning, guardrails).
• Experience with prompting patterns, structured outputs (JSON schemas), evaluation, and reducing hallucinations.
• Ability to design agent workflows for:
o Test generation/augmentation o Requirements review and completeness validation
o Report generation and summarization GitHub platform + GHCP (Copilot) for engineering workflows
• Strong proficiency with GitHub Copilot in day-to-day development.
• Deep experience with GitHub platform capabilities:
o GitHub Actions (CI/CD pipelines, reusable workflows, composite actions)
o PR checks, branch protections, CODEOWNERS, templates
o Automation via GitHub APIs/webhooks (as needed) Test automation engineering (framework expertise)
• Advanced experience designing and implementing automation with:
o Karate (API testing, contract-like checks, data-driven testing, mocks)
o Playwright (UI automation, selectors strategy, parallelization, trace/video artifacts)
• Strong understanding of test design and coverage:
o Happy path scenarios
o Negative/validation scenarios
o Edge/boundary scenarios
o Data setup/teardown strategies and test isolation Cross-service reporting and data aggregation
• Proven ability to aggregate and normalize results from multiple microservices and multiple pipelines.
• Experience producing actionable automated reports (trend analysis, failure clustering, service correlation). Automated requirements review agents
• Experience implementing automated checks that validate:
o Acceptance criteria completeness
o Required test data and environment dependencies
o Non-functional requirements (performance, security, observability) when applicable Deliverables / What success looks like (for the posting)
• A reusable agentic testing automation kit adopted by multiple teams.
• Published coverage standards + templates and onboarding documentation.
• A working GenAI-assisted reporting pipeline aggregating results across microservices.
• Automated quality gates integrated into GitHub workflows that measurably reduce story churn.
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