AI Evaluation Engineer
Job in
Washington, District of Columbia, 20022, USA
Listed on 2026-09-12
Listing for:
Tactical Edge
Full Time
position Listed on 2026-09-12
Job specializations:
-
Quality Assurance - QA/QC
IT QA Tester / Automation, AI QA / Validation Engineer
Job Description & How to Apply Below
Ensure enterprise AI platforms and solutions are reliable, tested, and production-ready.
Remote / Hybrid Security, Quality & Reliability Full-time
Role OverviewThe QA Engineer is responsible for ensuring Tactical Edge's AI platforms and customer solutions meet high standards of quality and reliability before and after deployment. The role collaborates closely with engineering, product, AI, and delivery teams to identify risks early, define test strategies, and ensure systems behave predictably in production.
What You'll Do- Define and execute test strategies for platforms and AI-powered solutions.
- Validate functional, integration, and regression behavior across systems.
- Collaborate with engineering and product teams early in the development lifecycle.
- Identify edge cases, failure modes, and risk areas in complex systems.
- Contribute to test automation, tooling, and quality processes.
- Support release readiness and production validation.
- Analyze issues from production and help prevent regressions.
- Continuously improve QA practices and documentation.
- Experience in QA for production software systems.
- Strong understanding of testing methodologies and quality practices.
- Ability to think in terms of systems, workflows, and edge cases.
- Comfort working with developers, product managers, and delivery teams.
- Attention to detail combined with pragmatic judgment.
- Familiarity with test automation and CI/CD pipelines.
- Bonus:
Experience testing AI-driven, data-heavy, or distributed systems.
Reliable systems over rushed releases
Enterprise-firstQuality, trust, and predictability
Quality by designEarly involvement, not late fixes
Small teams, high ownershipAutonomy with accountability
What You'll Get- Ownership of quality for production AI systems
- Exposure to enterprise-scale platforms and deployments
- Cross-functional collaboration with product, AI, and engineering teams
- Flexible work setup where applicable
- 3 Cross-functional interview (engineering/product perspective)
- 4 Final conversation
We value thoughtful testing, early risk detection, and clear communication.
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