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🤖 15+ AI Agents working for you. Find jobs, score and update resumes, cover letter, interview questions, missing keywords, and lots more.
Meril • Vapi, Gujarat, India
Role & seniority: Senior AI Platform QA Engineer (3–6 years experience)
Location & work type: Vapi, Gujarat — on-site (work from office)
Frontend: Next.js, React (SSR/CSR, hooks), Browser DevTools
Backend/API: REST/GraphQL, Node.js/Python testing, Postman/curl
Data & AI: Databases (CRU D, transactions), vector stores (Pinecone, FAISS), LLMs (OpenAI API, Ollama), RAG, prompt engineering
AI testing: LLM workflows, agentic logic, hallucination checks, model evaluation, fine-tuning pipelines
Testing tooling: Jira, Confluence; automation (Playwright, Cypress, PyTest)
Full-Stack & architecture testing: validate frontend, backend APIs, data integrity, and multi-user concurrency; identify edge cases and performance bottlenecks
AI & LLM module validation: assess relevancy, vector search quality, agent workflows, loop/edge-case handling, and hallucinations; evaluate model outputs
Quality ownership & engineering: code review for testability, design scalable test cases, ensure production readiness with logging, monitoring, and failover validation
3–6 years QA in full-stack web apps
Next.js/React deep knowledge (SSR/hydration, state management)
API testing expertise (Postman/curl), Node.js/Python logic
Hands-on AI/LLM experience: OpenAI API or Ollama, vector DBs (Pinecone/Weaviate), RAG, prompt engineering
Concurrency, race conditions, and system-level testing
Jira
Job Description: Senior AI Platform QA Engineer (Patent Tech) Experience = 3-6 Years Job Location = Vapi, Gujarat Job setting = Work from office
About the Role 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.
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.
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: 3–6 years in QA Engineering, with significant experience in Full-Stack web applications.
Frontend Mastery: Deep understanding of Next.js/React (SSR, hydration, client-side hooks) and Browser DevTools.
Backend & API: Expert at testing APIs (Postman, curl) and understanding Node.js/Python logic.
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 system-level 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.