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AI Developer

  • Virginia, McLean

  • 09/14/2026

  • Contract

  • Active

Job Description:

  • Job Summary
    We are seeking an AI Developer with strong expertise in Generative AI, LLM-powered agents, GitHub Copilot, and test automation engineering. The role will focus on designing reusable agentic testing patterns, developing AI-assisted test generation and maintenance capabilities, establishing scalable automation standards, and delivering GenAI-driven quality reporting across microservices. The ideal candidate will combine hands-on experience with agent workflows, prompt engineering, structured outputs, evaluation and guardrails with advanced Karate and Playwright automation expertise.

    Key Responsibilities
    • Design and implement LLM-powered agents supporting tool use, multi-step reasoning, guardrails, structured outputs, and reliable workflows.
    • Develop prompting patterns, evaluation approaches, and techniques for reducing hallucinations and improving AI-generated outputs.
    • Design agent workflows for test generation and augmentation, requirements review and completeness validation, report generation, and summarization.
    • Leverage GitHub Copilot extensively in day-to-day software development and engineering workflows.
    • Design and implement reusable agentic testing patterns that can be adopted across multiple Underwriting teams and expanded to other domains.
    • Create reference implementations, sample repositories, and templates for AI-assisted test generation, test maintenance, and failure analysis.
    • Develop test generation capabilities using requirements, APIs, contracts, and schemas.
    • Build test maintenance capabilities for updating selectors and contracts and assisting with flaky test triage.
    • Develop failure analysis capabilities for root-cause suggestions, log correlation, and defect drafting.
    • Establish standard architecture for test organization, tagging, data management, and execution across UI, API, and service layers.
    • Define and publish coverage standards, including minimum coverage expectations, test-type mix, risk-based prioritization, and traceability to requirements.
    • Develop reusable test plan, test case/specification, and Definition of Ready/Definition of Done templates.
    • Establish scalable tagging and metadata strategies covering features, services, risk, priority, and data sensitivity.
    • Build automated reporting that aggregates test execution, service health, and defect signals across multiple microservices.
    • Generate GenAI-driven release readiness narratives, failure clustering and trend analysis, and “What changed?” insights using commit and pull request correlations.
    • Deliver quality insights through dashboards, Markdown summaries in pull requests, and CI-generated artifacts.
    • Build automated review agents that evaluate user stories and requirements for completeness, clarity, testable outcomes, data needs, dependencies, privacy considerations, and environment requirements.
    • Integrate AI-powered quality gates into development workflows using pull request checks, issue templates, and GitHub Actions to reduce churn and rework.

    Required Qualifications
    • Hands-on experience building LLM-powered agents with tool use, multi-step reasoning, and guardrails.
    • Strong understanding of prompting patterns, structured outputs including JSON schemas, AI evaluation, and hallucination reduction techniques.
    • Strong proficiency with GitHub Copilot in day-to-day development.
    • Advanced experience designing and implementing test automation using Karate for API testing, contract-like checks, data-driven testing, and mocks.
    • Advanced experience with Playwright for UI automation, selector strategies, parallelization, and trace/video artifacts.
    • Experience designing reusable agentic testing patterns and reference implementations.
    • Strong understanding of test generation, maintenance, failure analysis, and AI-assisted quality engineering workflows.
    • Experience establishing test architecture and standards across UI, API, and service layers.
    • Experience defining coverage standards, test strategies, risk-based prioritization, and requirements traceability.
    • Experience creating test plans, test specifications, quality checklists, and scalable test metadata/tagging strategies.
    • Experience aggregating test execution, service health, and defect data across microservices.
    • Experience developing GenAI-driven reporting, quality insights, and release readiness summaries.
    • Experience integrating automated quality gates into CI/CD and development workflows.

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