Position Title: Agentic AI Developer
Location: McLean, VA
Duration: 6 months assignment with possible extension
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.
2) 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.
3) 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).
4) 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.
EEO:
Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.