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# AI Developer

- **Company:** Compunnel Inc.
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Continuous Integration, Github, Metadata, Microsoft UI Automation, Markdown, Requirements Traceability, Software Engineering, Test Case, Test Execution Engine, Strategies of Testing, GitHub Copilot, Large Language Models, Prompt Engineering, Playwright, Data Management, Microservices
- **Published:** September 14, 2026
- **Apply:** https://www.dice.com/job-detail/441ba8ab-50e0-4e34-820b-823e184bdcb7

## About the Role

8+ years of experience
Bachelors Degree

Skills
LLM-Powered Agent Development
Prompt Engineering
GitHub Copilot
Karate API Test Automation
Playwright UI Automation
Test Generation from Requirements, APIs, Contracts, and Schemas
Test Architecture Across UI, API, and Service Layers
Automated Reporting and Quality Dashboards
CI/CD Quality Gate Integration
Microservices Testing
Test Strategy and Standards Definition
Test Planning and Specification Authoring
Requirements Traceability
Failure Analysis and Root-Cause Investigation
Quality Reporting and Release Readiness Insights
Agentic Testing Pattern Design
Test Metadata and Tagging Strategy

## Description

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