Lead AI Engineer

Lead AI Engineer – Agentic Test Automation Tysons, VA All candidates selected for an interview are required to complete our mandatory identity verification process. Job Description 1) 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:

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. 2) Coverage standards, templates, and governance Define and publish coverage standards (what "good" looks like) including: Minimum coverage expectations by service/component 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 plan templates 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. 3) GenAI-assisted reporting and quality insights across microservices Build automated reporting that aggregates test + service data across multiple microservices, such as: Test execution results (Karate/Playwright + CI runs) Service health signals (logs/metrics/traces if available) Defect signals (issue tracker metadata if available) Generate GenAI-driven summaries : Release readiness narratives 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). 4) “Quality gates” via agents Build automated review agents that evaluate user stories/requirements for minimum required clarity and data before development/testing starts: Required fields present (acceptance criteria, testable outcomes, data needs, dependencies) Ambiguity detection and missing edge cases 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: Test generation/augmentation Requirements review and completeness validation 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: GitHub Actions (CI/CD pipelines, reusable workflows, composite actions) PR checks, branch protections, CODEOWNERS, templates Automation via GitHub APIs/webhooks (as needed) Test automation engineering (framework expertise) Advanced experience designing and implementing automation with: Karate (API testing, contract-like checks, data-driven testing, mocks) Playwright (UI automation, selectors strategy, parallelization, trace/video artifacts) Strong understanding of test design and coverage: Happy path scenarios Negative/validation scenarios Edge/boundary scenarios 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: Acceptance criteria completeness Required test data and environment dependencies 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. RIT Solutions