Agentic AI Automation Engineer

Agentic Ai Developer

Position Title:

Agentic AI Developer

Location:

McLean, VA Duration: 6 months assignment with possible extension

• 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.

• 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.

• 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).

• 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, Git Hub Actions) to reduce churn and rework.

• 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

• Strong proficiency with Git Hub Copilot in day-to-day development.
• Deep experience with Git Hub platform capabilities:
• Git Hub Actions (CI/CD pipelines, reusable workflows, composite actions).
• PR checks, branch protections, CODEOWNERS, templates.
• Automation via Git Hub APIs/webhooks (as needed).

• 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.

• Proven ability to aggregate and normalize results from multiple microservices and multiple pipelines.
• Experience producing actionable automated reports (trend analysis, failure clustering, service correlation).

• Experience implementing automated checks that validate:
• Acceptance criteria completeness.
• Required test data and environment dependencies.
• Non-functional requirements (performance, security, observability) when applicable.

• 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 Git Hub 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."