AI Developer
AI DeveloperLocation – McLean, VA ContractJob 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/augmentationo Requirements review and completeness validationo Report generation and summarizationGitHub 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/componento 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 templateso 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 narrativeso Failure clustering and trend analysiso "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 caseso Data/privacy considerations and environment needs • Integrate gates into workflow (PR checks, issue templates, GitHub Actions) to reduce churn and rework.