We're looking for an AI QA Engineer to own quality across our growing portfolio of AI-powered products custom dashboards, AI CRMs, RFP platforms, lead-gen systems, agentic workflows, and automations delivered to agency clients across the US, UK, and AU.

This role spans both the AI layer (prompts, agents, RAG, model outputs) and the full application stack around it (web app UI, APIs, databases, integrations, multi-tenant access).

You'll catch issues that traditional QA misses and the ones that pure ML evaluation misses the messy middle where a working model still ships a broken product.

Key Responsibilities :

AI & LLM Quality :

- Design test strategies for LLM-powered features, agents, RAG systems, and workflow automations (n8n, custom pipelines, MCP-based systems).

- Build and maintain golden datasets, evaluation suites, and prompt regression tests so model or prompt changes don't silently break production.

- Validate output quality across factual accuracy, format adherence, tone, hallucination rate, instruction-following, and refusal behavior.

- Test agentic and multi-step workflows end-to-end tool calls, handoffs, error recovery, loop detection, cost and latency under load.

- Stress-test RAG pipelines : retrieval accuracy, chunking quality, context relevance, citation correctness.

- Run adversarial testing prompt injection, jailbreak attempts, malformed inputs, edge-case client data.

Custom App & Platform Quality :

- Own end-to-end QA for custom-built applications : dashboards, AI CRMs, client portals, and internal tools.

- Functional, regression, cross-browser, and responsive testing across web UIs (Next.js, React, similar stacks).

- API testing for REST endpoints, webhooks, and third-party integrations (CRMs, email, calendars, payment, file storage).

- Database and data-integrity testing especially for multi-tenant architectures where one client's data must never leak to another.

- Auth and access-control testing : SSO, OAuth, role-based permissions, session handling.

- Performance, load, and stability testing on production-bound features.

- File upload, processing, and storage flows a recurring failure point in AI products.

Cross-Cutting :

- Monitor production systems for drift, degradation, errors, and cost/token anomalies; set up observability and alerting.

- Collaborate with AI engineers, full-stack developers, prompt designers, and delivery leads to catch issues before they hit clients.

- Automate testing in Python and/or JavaScript; integrate into CI/CD pipelines.

Required Skills & Qualifications :

- Bachelor's degree in Computer Science, IT, Engineering, or related field.

- 3+ years QA experience covering both web applications and AI-enabled products, with at least 1 year hands-on testing LLM-powered features, AI workflows, or agent systems.

- Strong Python skills for evaluation harnesses and automation; working JavaScript/TypeScript familiarity for web app testing.

- Hands-on experience with at least one LLM evaluation framework : Promptfoo, DeepEval, RAGAs, LangSmith, Braintrust, or similar.

- Web app testing experience with Playwright, Cypress, or Selenium.

- API testing with Postman, REST Assured, or equivalent.

- Solid understanding of prompt engineering, prompt regression testing, and golden-dataset design.

- Database fundamentals (SQL, basic schema reasoning) enough to verify data flows and spot multi-tenancy bugs.

- Familiarity with LLM observability tools (Langfuse, Helicone, LangSmith) and token/cost/latency monitoring.

- Git, CI/CD principles, and at least one cloud platform (AWS, GCP, or Azure).

- Working knowledge of vector databases, embeddings, and how RAG systems fail.

Nice to Have :

- Experience testing multi-tenant SaaS platforms or client-facing custom apps.

- Workflow automation platforms (n8n, Make, Zapier) or agent frameworks.

- Exposure to MCP (Model Context Protocol) servers or tool-calling architectures.

- Familiarity with Supabase, Postgres, or similar backends commonly used in our stack.

- Experience with Claude, OpenAI, or open-source LLM APIs in production.

- Agency or client-services background you understand what "client-ready" means.

- Prompt injection / red-teaming experience.

- SOC 2 or compliance-testing exposure.

Soft Skills :

- Sharp critical eye you assume AI output (and the app around it) is broken until you've verified it.

- Comfortable working in ambiguity; AI QA doesn't have settled playbooks yet.

- Clear written communication bug reports for AI failures need precise reproduction steps.

- Collaborative across AI engineers, full-stack devs, delivery managers, and account leads.

- Proactive about catching issues before they reach the client demo.

- Prior experience working closely with AI/engineering teams is required.