Job Description:
Required Technical Skills :
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:
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)
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.