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
