Lead AI Engineer – Agentic Test Automation

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.

• 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.
• 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).
• “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.