Role OverviewWe're looking for a Lead / Senior / Staff QA Engineer to own quality for Nexxa's AI agent systems - products that plan, call tools, and take multi-step actions autonomously in industrial environments. This isn't traditional UI testing: you'll be designing evaluation frameworks for non-deterministic, tool-using systems, building golden datasets, catching regressions in reasoning quality, and stress-testing agent behavior under adversarial and real-world edge-case conditions.
You'll work closely with ML engineers, backend engineers, and Forward Deployed Engineers to define what "good" looks like for an agent operating in high-stakes industrial settings, then build the infrastructure and processes to measure it continuously.
Key Responsibilities- Design and build evaluation harnesses and regression suites for LLM-based agents, covering reasoning quality, tool-call correctness, task completion, and multi-turn coherence.
- Develop golden datasets and labeled test sets, including edge cases, ambiguous inputs, and adversarial prompts specific to industrial and operational contexts.
- Define and track quality metrics beyond simple accuracy - groundedness, hallucination rate, task success rate, latency/cost tradeoffs, and safety violations.
- Build automated pipelines that run evals on every model, prompt, or tool-integration change, and integrate them into CI/CD.
- Conduct structured red-teaming and adversarial testing (prompt injection, jailbreaks, tool misuse, unsafe actions) in partnership with security teams.
- Test agent behavior across the full action loop - planning, tool selection, tool execution, error recovery, and final output - not just the final response.
- Investigate and triage failures where the root cause could be the model, the prompt, the tool/API, or the orchestration logic.
- Partner with ML and backend engineers to translate eval failures into actionable, reproducible bug reports.
- Establish quality bars and sign-off criteria for new agent capabilities before they reach customer environments.
- Mentor other engineers on testing strategies specific to probabilistic, LLM-driven systems.
- Advocate for testability and observability in agent architecture from day one.
Qualifications- 5+ years in QA/SDET roles, with demonstrated ownership of test strategy for complex systems.
- Hands-on experience testing LLM-based products, chatbots, or AI agents - you understand why traditional deterministic test assertions break down for generative systems.
- Practical experience with eval frameworks or tooling (e.g., promptfoo, DeepEval, RAGAS, LangSmith) or a track record of building your own.
- Strong scripting/programming ability (Python preferred) to build test automation, data pipelines, and eval tooling.
- Understanding of how LLM agents work: prompting, tool/function calling, context management, RAG, memory, and orchestration frameworks.
- Experience designing test data and labeled datasets, including sourcing, sampling, and managing dataset drift over time.
- Familiarity with LLM-specific failure modes: hallucination, prompt injection, context poisoning, tool misuse, goal drift, and non-determinism.
- Comfortable operating in ambiguity - defining what "correct" means for a task when there's no single right answer.
- Strong written communication skills for turning fuzzy quality signals into clear, actionable findings for engineering and product stakeholders.
Preferred- Experience with human-in-the-loop evaluation workflows (labeling pipelines, inter-rater reliability, rubric design).
- Background in ML/data science sufficient to read model evals and statistical significance.
- Experience red-teaming or doing adversarial/security testing on ML systems.
- Familiarity with observability/tracing tools for LLM applications (e.g., LangSmith, Arize, Langfuse, Weights & Biases).
- Experience testing AI systems in industrial, IoT, or operational technology (OT) environments.
- Prior experience setting up eval infrastructure from scratch at a startup or fast-moving team.
- What We're Looking For A QA engineer who wants to define what quality means for autonomous, real-world AI systems.
- Someone who can build rigorous evaluation infrastructure for problems that don't have a single right answer.
- A systems thinker who enjoys turning ambiguous agent behavior into measurable, trustworthy signals.
- A strong collaborator who partners well with ML engineers, backend engineers, and Forward Deployed teams.
