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EI

Lead AI Engineer

Expert In Recruitment Solutions
🇺🇸 United States
On-site
Staff / Principal
1 month ago
  • AI
  • Test Automation
  • triage
  • Gherkin
  • Machine Learning
  • Microservices
  • Playwright
  • GitHub Actions
  • JSON
  • GitHub
  • Copilot
  • CI/CD
  • Webhooks
1 month ago
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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 implementagentic testing patterns that can be adopted by multiple Underwriting teams (and later other domains).
· Createreference 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 astandard architecture for test code organization, tagging, data management, and execution across UI + API + service layers.

2) Coverage standards, templates, and governance
  • Define and publishcoverage 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
  • Providetemplates 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 ascalable 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 thataggregates 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)
  • GenerateGenAI-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 forminimum 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 buildingLLM-powered agents (tool-using, multi-step reasoning, guardrails).
  • Experience withprompting patterns, structured outputs (JSON schemas), evaluation, and reducing hallucinations.
  • Ability to designagent 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 withGitHub Copilot in day-to-day development.
  • Deep experience with GitHub platform capabilities:
oGitHub 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:
oKarate (API testing, contract-like checks, data-driven testing, mocks)
oPlaywright (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 frommultiple 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 reusableagentic testing automation kit adopted by multiple teams.
  • Publishedcoverage standards + templates and onboarding documentation.
  • A workingGenAI-assisted reporting pipeline aggregating results across microservices.
  • Automatedquality gates integrated into GitHub workflows that measurably reduce story churn.

Lead AI Engineer · Expert In Recruitment Solutions

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