AI Quality Engineer

Key Responsibilities

• Build and maintain automated tests for AI agent workflows, APIs, tools, telemetry backed analysis, remediation flows, and ticketing behavior.
• Design evaluation suites for LLM and agentic behavior, including expected-answer checks, rubric-based grading, regression datasets, tool-call validation, and safety/approval checks.
• Use or help implement evaluation frameworks such as Pydantic Evals / Pydantic AI, Strands Evals, Lang Smith, Deep Eval, Ragas, promptfoo, or similar tools.
• Validate multi-turn support scenarios, clarification flows, knowledge retrieval, script/remediation recommendations, escalation paths, and failure handling.
• Test on-device agent behavior where needed, including Windows service/tray behavior, telemetry collection, anomaly detection, local remediation handoff, logs, and resource impact.
• Debug quality issues directly by reading logs, tracing requests, reproducing failures, and making small code/test changes without heavy engineering hand-holding.
• Partner with engineering and product to define release gates, quality metrics, evaluation rubrics, and confidence thresholds for pilot readiness.
• Contribute to CI quality checks, test fixtures, mocked integrations, regression suites, and test data management.
• Identify risks in AI behavior, including hallucinated diagnosis, unsafe remediation suggestions, missing consent, weak ticket summaries, brittle tool use, and poor escalation behavior.

• 4+ years of experience in QA engineering, SDET, test automation, software engineering, or similar hands‑on quality roles.
• Strong Python experience, including writing production‑quality tests and debugging application code.
• Experience testing backend services, APIs, async workflows, integrations, logs, and distributed systems.
• Demonstrated ability to work independently in a codebase, identify root causes, and make targeted fixes or test improvements.
• Experience with modern test frameworks such as pytest, unittest, Playwright, xUnit, or similar.
• Comfortable testing ambiguous AI/non‑deterministic behavior using datasets, rubrics, assertions, metrics, and regression baselines.
• Ability to distinguish product bugs, prompt/model behavior issues, data issues, integration failures, and test harness problems.
• Strong written communication for documenting repro steps, risks, test plans, and release readiness.

• Direct experience with Pydantic, Pydantic AI, or Pydantic Evals.
• Experience testing LLM applications, agentic systems, RAG, tool calling, MCP servers, or AI assistants.
• Experience building eval datasets, LLM‑as‑judge flows, deterministic evaluators, and regression dashboards.
• Windows endpoint, device telemetry, Power Shell, .NET, MQTT, or on‑device agent testing experience.
• Experience with IT support workflows, Service Now, ticketing, endpoint management, or enterprise support tooling.
• Ability to contribute small application changes, not only test changes.