Senior AI Quality Engineer

Novara provides safety, operational risk management, and sustainability software that empowers organizations to identify and resolve issues before they become incidents. Through the Flex and Risk Management Center platforms, Novara helps organizations manage operational risk proactively with a system they can configure to fit their operations, data that unifies risk and safety intelligence, and tools that increase workforce participation. Novara’s combination of software, training, and AI-powered tools put people and safety first while protecting critical operations. Position Description: AI is core to where our product is heading, and the quality bar for it has to be as high as anything else we ship. This role owns that bar.

You will define what "working" means for non-deterministic systems, build the tooling to prove it, and give our teams the confidence to move faster on AI-powered features and agent-based products. You will partner closely with product, engineering, and the broader QA team to bring rigor to how we test LLM-driven and agentic workflows, while also contributing to traditional automation coverage where it matters. This is a hands-on IC role reporting into the QA Manager, with high visibility into AI initiatives across the company. Responsibilities: - Design and maintain evaluation frameworks for LLM outputs and agentic workflows, including regression suites, golden datasets, and scoring rubrics - Build test harnesses that catch hallucinations, tool-calling failures, prompt regressions, and unsafe or off-policy behavior before they reach production - Define measurable quality criteria for agent reliability: task completion, factual grounding, latency, cost, and reasoning quality - Integrate evaluation runs into CI/CD so model, prompt, and agent changes are gated the same way code changes are - Partner with engineers on observability and tracing for agent runs, so failures are diagnosable rather than mysterious - Contribute to conventional API and end-to-end automation where AI features sit inside larger product flows - Help shape the team's shared playbook for testing AI features, and coach other QA engineers as agentic work spreads across scrum teams Knowledge, Experience, Requirements: - 6+ years in QA, SDET, or test automation, with real production automation shipping - Hands-on experience testing LLM-based or agentic systems: building evals, working with LLM-as-judge patterns, prompt regression testing, or agent trajectory analysis - Prior experience in a shift-left, embedded QA model - Comfort with at least one modern automation stack (Playwright, Cypress, or similar) and a typed language, TypeScript preferred, Python fine - Deep experience with test frameworks such as vitest, jest, or pytest, and comfort building custom test harnesses rather than only running off-the-shelf suites - API-first testing mindset, including REST and Postman or equivalent - Fluency in API testing, including recording proxies and observing service-to-service traffic - Working knowledge of CI/CD pipelines, GitHub Actions a plus, and how to plug evals into them - Familiarity with cloud secret managers and disciplined handling of sensitive test data in restore-from-prod environments - Ability to reason clearly about probabilistic systems: variance, sample sizes, confidence, and when a flaky result is signal rather than noise Preferred Qualifications: Not required, but they will stand out: - Experience with eval tooling such as Langfuse, Braintrust, LangSmith, Ragas, or DeepEval - Familiarity with RAG systems, vector stores, or tool-calling frameworks - Background in test data strategy for AI, including synthetic data generation - Exposure to Datadog or a similar observability platform Tech Stack: TypeScript, Playwright, vitest, Postman, GitHub Actions, Jira/Xray, Datadog, AWS, plus emerging AI evaluation tooling.