AI Quality Engineer

About the Role Testing AI systems is a fundamentally different problem than testing traditional software. Outputs are non-deterministic. "Correct" is often a spectrum. And the failure modes—hallucinations, drift, prompt injection—don't show up in unit tests. We need an engineer who understands this and can build the testing strategies, evaluation frameworks, and quality infrastructure to keep our agents reliable in production. As an AI Quality Engineer, you'll design how we test intelligent agents, agentic workflows, and Foundation Layer capabilities. This is not a manual QA role—you'll write code, build evaluation pipelines, and create automated testing frameworks that run in CI/CD. You'll define what "quality" means for AI systems at AGS and build the systems to measure it. You'll work across every solution the team builds, which means you'll have broad visibility into the architecture and deep understanding of how our agents behave in the real world. If you're an engineer who cares about quality and wants to solve testing problems that most teams haven't figured out yet, this is the role. Responsibilities   Testing Strategy & Design Define testing strategies for AI agents, conversational interfaces, and agentic workflows Design behavioral test suites for non-deterministic outputs—where "correct" isn't binary Build evaluation frameworks that measure groundedness, factuality, relevance, and task completion Identify failure modes specific to AI systems: hallucinations, prompt injection, context window limitations, drift Develop testing approaches for each architecture pattern: RAG, function calling, human-in-the-loop, autonomous workflows   Test Automation & Infrastructure Build automated evaluation pipelines that run as part of CI/CD Create test harnesses for LLM-based systems—mocking, fixtures, and reproducible test scenarios Develop regression suites that detect quality degradation when prompts, models, or data change Build monitoring and alerting for production agent quality (accuracy, latency, error rates) Maintain test infrastructure: test data management, environment setup, reporting dashboards   Evaluation & Metrics Define quality metrics for each solution—what to measure and what thresholds matter Build and maintain evaluation datasets (ground truth, reference outputs, edge case collections) Conduct systematic prompt evaluation when prompts or models change Track quality trends over time and identify when re-evaluation is needed Report quality metrics to the team and stakeholders in clear, actionable terms   Collaboration & Quality Culture Partner with AI Solutions Engineers to define testability requirements during design Work with AI Solutions Analysts to translate acceptance criteria into test scenarios Review solution designs from a quality and testability perspective Advocate for quality practices across the team—testing isn't an afterthought, it's part of delivery Contribute to incident response by diagnosing quality failures and building regression tests Qualifications   Required 3–7 years of software engineering or quality engineering experience Strong programming skills in Python and/or TypeScript—you write test code, not just test cases Experience designing and building automated test frameworks Understanding of AI/ML systems—you know why testing LLM outputs is different from testing deterministic code Experience with CI/CD pipelines and integrating automated tests into build processes Ability to reason about non-deterministic systems and design meaningful quality metrics Strong analytical skills—you can look at agent outputs and determine whether they're good enough   Preferred Experience testing AI/ML applications, conversational interfaces, or chatbots Background in LLM evaluation: prompt testing, groundedness scoring, factuality checking Familiarity with evaluation frameworks (DeepEval, Ragas, custom evaluation pipelines) Experience with Microsoft Power Platform (Power Automate, Copilot Studio) testing Background in Azure services and cloud-based test infrastructure Experience with load testing and performance testing for API-based systems Familiarity with staffing, HR tech, or workforce management domains   Technology Stack Languages: Python, TypeScript Platforms: Azure (Container Apps, Functions, AI Services), Microsoft 365 Testing: pytest, evaluation frameworks (DeepEval, Ragas, custom), load testing tools AI/ML: LLM evaluation, prompt testing, RAG evaluation, behavioral testing Data: REST APIs, Dataverse, SQL Tools: Git, GitHub, CI/CD pipelines, Docker, monitoring/alerting (Application Insights) We don't expect expertise in everything. AI quality engineering is a new discipline—we expect strong engineering fundamentals and the ability to figure out new problems.   What We're NOT Looking For Manual testers who write test cases in spreadsheets QA professionals who treat testing as a gate at the end of development rather than a practice woven into it People who expect deterministic pass/fail for every test—AI quality requires nuance Engineers who test to the spec but don't think about how real users will break things   What Makes You Stand Out You've tested a system where "correct" was hard to define—and found a way to measure it anyway You write test code that's as clean and maintainable as production code You think about edge cases that nobody else considers You can explain why a particular quality metric matters and what threshold makes sense You've built test automation that actually caught regressions before they hit production You're comfortable saying "this isn't good enough" and backing it up with data   What We're Building The AI Engineering team delivers intelligent solutions for AGS's global clients: Intelligent Agents — Conversational AI that helps hiring managers, recruiters, and internal teams get work done faster Agentic Workflows — Automated processes where AI executes tasks with human oversight Foundation Capabilities — Reusable AI services that power multiple solutions You'll make sure these systems work reliably—not just at launch, but as models change, data evolves, and usage scales.   Career Growth AI quality engineering is an emerging discipline with no ceiling. Growth paths include: Depth — Become the team's authority on AI evaluation and testing methodology, influencing quality standards across the organization Breadth — Move into a Senior or Lead AI Solutions Engineer role, bringing your quality mindset to architecture and delivery Specialization — Build expertise in areas like LLM security testing, AI safety, or evaluation research As a workplace, we focus on relationships – with each other, our clients and our candidates - in fact serving others is one of our core values. We support open communication and recognize that giving constructive criticism can be even harder than receiving it. We appreciate the fearless and the passionate, who force us to be better. Everything we do sits on a pillar of diversity - diverse perspectives, backgrounds and ideas drive innovation and make us successful. See what it’s like to work at AGS by searching #LifeAtAGS on any social network.