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: 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. 2) 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.
3) 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).
4) 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
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 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.
Numbers & Facts
Location
McLean, VA
Skills
Application Programming Interface (API)unmatched
Automationunmatched
Automotive Repair and Maintenanceunmatched
Customer/Client Researchunmatched
Data Managementunmatched
Failure Analysisunmatched
GitHubunmatched
JSONunmatched
Microservicesunmatched
Root Cause Analysisunmatched
Test Automationunmatched
Test Designunmatched
Test Patternsunmatched
Test Plan/Scheduleunmatched
Training Data Setsunmatched
Trend Analysisunmatched
Underwritingunmatched
User Interface/Experience (UI/UX)unmatched
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