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
1. Full-Stack & Architecture Testing
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: 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.
AI Knowledge: Hands-on 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 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.
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