Job Description

About the Company

We are looking for a highly skilled QA Engineer for our (AI Platform) 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.

About the Role

A short paragraph summarizing the key role responsibilities.

Responsibilities

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.
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.
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.

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.

Required 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.

Preferred Skills

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.

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.