Junior Software Engineer In Test Jobs in Alpharetta, GA

About GreenSky

GreenSky, LLC, headquartered in Atlanta, is a leading technology company Powering Commerce at the Point of Sale® for a growing ecosystem of merchants, consumers, and banks. GreenSky's highly scalable, proprietary and patented technology platform enables merchants to offer frictionless promotional payment options to consumers, driving increased sales volume and accelerated cash flow. The GreenSky® Program is operated on behalf of, and financing is offered and made by, federally insured, federal or state chartered financial institutions, which leverage GreenSky's technology to offer loans to primarily super-prime and prime consumers nationwide. Since GreenSky's inception, nearly 6 million consumers have financed nearly $70 billion of commerce using GreenSky's real time "apply and buy" technology.

Location: Alpharetta, GA (Hybrid: 3 Days per week)

This role requires unrestricted work authorization now and in the future, as visa sponsorship and sponsorship transfers are not available.

About the role:

We are seeking a highly technical and innovative AI Software Engineer in Test (AI SEIT) to help modernize quality engineering through test automation, AI-assisted development, data validation, and intelligent software testing practices.

This role will leverage AI as an AI-enabled engineering assistant to accelerate test framework development, analyze codebases, generate and refactor automated tests, debug failures, inspect logs, improve coverage, and support rapid feedback loops across the SDLC. AI is an agentic coding tool that can read codebases, edit files, run commands, and integrate with development tools across terminal, IDE, desktop, and browser environments.

The ideal candidate will combine strong software engineering skills, quality engineering discipline, data validation expertise, and practical AI tool adoption to improve application quality, delivery velocity, and release confidence.

AI-Assisted Test Engineering:

• Use AI tools to accelerate test automation development, including generating, modifying, and refactoring test scripts across web, mobile, API, backend, and data platform applications.
• Leverage AI tools to analyze existing codebases, identify test coverage gaps, suggest edge cases, and improve automated regression coverage.
• Use AI to debug test failures, analyze stack traces, inspect logs, and recommend code or test corrections.
• Create repeatable AI workflows for test generation, defect triage, regression analysis, and automation maintenance.
• Provide verification criteria, test commands, build checks, and validation steps so AI agents can iteratively run checks and confirm whether changes pass or fail. Anthropic's AI best practices recommend giving Claude a way to verify its work through tests, builds, linters, scripts, or similar pass/fail signals.
• Partner with engineering teams to establish safe and governed usage patterns for AI-assisted test development.

Test Automation Development:

• Design, develop, and maintain comprehensive automated test frameworks for web, mobile, backend, API, data, and AI-enabled applications.
• Build scalable automation solutions using tools and frameworks such as Selenium, Playwright, Cypress, REST Assured, JUnit, TestNG, PyTest, or equivalent technologies.
• Work closely with developers, QA engineers, product managers, and business analysts to ensure high test coverage and rapid feedback loops.
• Develop reusable automation components, shared libraries, test utilities, and framework standards.
• Maintain automation suites for functional, regression, integration, end-to-end, API, performance, security, and data validation testing.

AI, GenAI, and Model Validation:

• Develop test strategies for AI-enabled applications, Generative AI workflows, LLM-powered features, RAG solutions, conversational AI, and agentic systems.
• Validate AI outputs for accuracy, relevance, completeness, hallucination risk, toxicity, policy alignment, and business rule compliance.
• Create automated evaluation datasets, prompt test suites, golden answer sets, and scoring rubrics.
• Build regression testing approaches for prompts, model