AI Quality Engineer
New
P
PhoenixTeamFederal AI/ML
100% Remote (U.S.), 8am–5pm ETFull-Time
Salary not disclosed
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Job Details
- Required Skills
- AWSPostgreSQLArtificial IntelligenceJavaSalesforceSeleniumCI/CDLangChain
Requirements
- Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent professional experience.
- Demonstrated experience in software quality assurance, test engineering, or a related discipline, including test plan design and defect management.
- Hands-on experience testing or validating AI-generated code, prototypes, or “vibe-coded” solutions for accuracy and reliability.
- Experience with automated testing tools and frameworks (e.g., Selenium) across web and enterprise applications.
- Experience with one or more of the following: Salesforce, AWS (including Lambda and Redshift), Java, or PostgreSQL, or a comparable enterprise technology stack.
- Familiarity with CI/CD pipelines and quality gates within modern software delivery practices.
- Strong analytical skills, with the ability to identify security, data-sensitivity, and compliance risks in AI-assisted solutions.
- Excellent written and verbal communication skills, with the ability to work across product, engineering, data, and business SME teams.
Responsibilities
- Design and implement AI-enabled solutions for customer modernization use cases, developing and integrating AI capabilities into existing systems and workflows.
- Establish and enforce a quality assurance framework — test plans, acceptance criteria, and regression suites — for validating AI-generated code, skills, and agents before they are promoted through the innovation pipeline.
- Guide the development of reusable AI skills and agents for customer modernization tasks, with particular emphasis on automated testing, security scanning, data sensitivity, and scalability.
- Collaborate with DevOps engineers to create and orchestrate long-running, multi-agent workflows, ensuring reliability, observability, and recoverability in production environments.
- Support creation of the innovation pipeline that moves AI-generated (“vibe-coded”) prototypes from sandbox concepts into secure, enterprise-grade applications.
- Support Value Engineers’ efforts by reviewing and quality-checking prototype outputs for accuracy, reliability, and adherence to standards prior to pipeline promotion.
- Continuously monitor AI-assisted development outputs for accuracy, reliability, and compliance drift, feeding findings back to Value Engineers and AI Engineers to improve prompts, skills, and agents over time.
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