Senior AI Platform QA Engineer (LLM & Full-Stack)

We are looking for a highly skilled Senior AI Platform QA Engineer to ensure the reliability, accuracy, and performance of our AI-based patent platform. You won't just follow test cases; you will "break" systems, analyze Next.js code flows, and validate complex LLM agentic workflows. This role requires a unique blend of Full-Stack technical QA (Next.js, APIs, Databases) and AI/LLM testing (RAG, Prompt Engineering, Hallucination detection). You will act as a quality gatekeeper, thinking like a developer to identify architectural flaws before they reach production. Key Responsibilities: Next.js Frontend: Perform deep functional and integration testing. Analyze components, hooks, and state management to identify SSR/CSR edge cases and performance bottlenecks. Backend & API: Validate REST/GraphQL API contracts, payload integrity, and authentication flows. Perform multi-user concurrent testing to identify race conditions. Database Integrity: Test CRUD operations, transactions, and rollbacks. Ensure data consistency across vector databases (Pinecone/FAISS) and relational schemas. 2. AI & LLM Module Validation Patent Search & RAG: Validate relevancy ranking, vector search accuracy, and the quality of retrieved context. Agent Workflows: Test LLM-powered multi-step agents for autonomy behaviors, "looping" issues, and edge-case handling. Model Evaluation: Evaluate outputs for hallucinations, factual accuracy (specifically for patent law), and consistency using tools like OpenAI/Ollama. Fine-Tuning Pipelines: Validate datasets and monitor training runs to benchmark model performance. 3. Quality Ownership & Engineering Code Review: Review frontend and backend code from a testability perspective, identifying anti-patterns and suggesting better error handling. Test Design: Write scalable, reusable test cases for complex multi-user workflows. Production Readiness: Validate logging, monitoring, and failover recovery. Analyze real-world failure scenarios and production bugs. Required Skills & Qualifications: Experience: 36 years in QA Engineering, with significant experience in Full-Stack web applications. Backend & API: Expert at testing APIs (Postman, curl) and understanding Node.js/Python logic. AI Knowledge: Handson experience with: LLMs: OpenAI API, Ollama, or local model orchestration. Vector Tech: RAG pipelines and vector databases (Pinecone, Weaviate, etc.). Prompt Engineering: Ability to identify issues with prompts and agentic logic. Testing Mindset: Proven ability to test for concurrency, race conditions, and systemlevel failures. Tools: Proficiency in Jira/TestRail and exposure to automation frameworks like Playwright, Cypress, or PyTest. JIRA + Confluence exposure must. Nice-to-Have Skills Familiarity with the Intellectual Property (IP) / Patent domain. Experience with Docker, CI/CD pipelines, and cloud platforms (AWS/GCP). Experience with LLM evaluation frameworks (e.g., RAGAS, DeepEval). Performance/Load testing exposure using tools like k6 or Locust. What We Expect From You You are a System Breaker: You don't just test features; you look for ways the system might fail under stress. You Think Like a Developer: You can read code to understand where the bugs are likely hiding. You are a Quality Advocate: You are comfortable challenging implementations when quality or user experience is at risk. You are AI-Curious: You stay updated on the latest in LLMs and agentic frameworks. What We Offer Opportunity to work at the intersection of Generative AI and LegalTech. A highly technical environment where QA is treated as an engineering discipline. Freedom to explore and implement new testing methodologies for AI. We are looking for a highly skilled Senior AI Platform QA Engineer to ensure the reliability, accuracy, and performance of our AI-based patent platform. You won't just follow test cases; you will "break" systems, analyze Next.js code flows, and validate complex LLM agentic workflows. This role requires a unique blend of Full-Stack technical QA (Next.js, APIs, Databases) and AI/LLM testing (RAG, Prompt Engineering, Hallucination detection). You will act as a quality gatekeeper, thinking like a developer to identify architectural flaws before they reach production. Key Responsibilities: Next.js Frontend: Perform deep functional and integration testing. Analyze components, hooks, and state management to identify SSR/CSR edge cases and performance bottlenecks. Backend & API: Validate REST/GraphQL API contracts, payload integrity, and authentication flows. Perform multi-user concurrent testing to identify race conditions. Database Integrity: Test CRUD operations, transactions, and rollbacks. Ensure data consistency across vector databases (Pinecone/FAISS) and relational schemas. 2. AI & LLM Module Validation Patent Search & RAG: Validate relevancy ranking, vector search accuracy, and the quality of retrieved context. Agent Workflows: Test LLM-powered multi-step agents for autonomy behavio