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
Why Join Us
Solve new problems — AI testing is an unsolved discipline. You'll invent approaches, not just follow playbooks
Broad visibility — Touch every solution the team builds and understand the full architecture
Write real code — Build evaluation frameworks and test infrastructure, not just test cases
Production systems — Test agents serving Fortune 500 clients, where quality actually matters
Shape quality culture — Define what quality means for AI at AGS and build the systems to enforce it
Growing field — AI quality engineering is early. Getting good at it now puts you ahead of the industry
About Allegis Global Solutions
We are founded on a culture that is passionate about transforming the way the world acquires talent by delivering client-focused solutions that make a difference for businesses worldwide. From refining how businesses manage their contingent workforce to strengthening employer brands to recruit top talent, our integrated solutions drive business results. As an industry leader, we draw upon decades of experience to design innovative tools, products, and processes. We develop competitive practices that position organizations for growth and we deliver the insight needed to succeed in today's global marketplace. 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.
FOR U.S. ONLY
AGS is an Equal Opportunity/Affirmative Action Employer (M/F/Disability/Veterans). We will consider all applications without regard to ra

Work arrangement
No