AI System Quality Assurance Engineer – Data & Agentic AI

ChiStats · Pune, Maharashtra

Job boardAI / ML₹8 LPA – ₹14 LPA

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Artificial Intelligence is transforming software testing, and here's your chance to be part of that revolution. ChiStats is hiring AI System Quality Assurance Engineers – Data & Agentic AI for its Pune office. If you have 1–3 years of experience in Automation Testing, API Testing, Data Validation, Integration Testing, Migration Testing, and AI-driven Testing, this role offers a unique opportunity to work on cutting-edge Agentic AI systems used in real-world insurance applications. Unlike traditional QA positions, this role focuses on validating AI-generated outputs, data integrity, intelligent workflows, APIs, business rules, and AI agent decision-making. If you want to build expertise in one of the fastest-growing domains in Quality Engineering, this opportunity deserves your attention. Job Details Company: ChiStats Position: AI System Quality Assurance Engineer – Data & Agentic AI Location: Pune, Maharashtra Work Mode: Full-Time (Office) Experience: 1–3 Years Interview Mode: Face-to-Face at Pune Office Open Positions: 2 Why This Role Is Great for Experienced QA Professionals Very few QA opportunities today provide direct exposure to Agentic AI, making this role especially valuable. Key Career Benefits Gain practical experience testing AI and LLM-powered systems. Work extensively on enterprise data validation and migration testing. Validate intelligent agent workflows and AI decisions. Collaborate directly with US-based Product and Engineering teams. Build expertise in one of the fastest-growing AI quality domains. Excellent pathway toward AI QA Engineer, AI Test Architect, or AI Validation Specialist roles. Learn real-world AI guardrails, hallucination detection, and AI evaluation techniques. As AI adoption accelerates across industries, professionals with AI testing expertise are expected to remain in exceptionally high demand. Key Responsibilities You'll contribute across multiple quality engineering areas, including: Validate AI-generated insurance applications. Test Agentic AI workflows. Perform API and Integration Testing. Validate complex data migrations. Compare source-to-target datasets. Verify business rule execution. Validate AI guardrails. Execute regression and migration testing. Verify JSON payloads and API responses. Investigate AI output inconsistencies. Build data validation scripts. Communicate quality risks to global stakeholders. Skills Required Besides the official requirements, recruiters generally value experience in: Automation Testing Manual Testing API Testing Integration Testing Migration Testing Data Validation JSON REST APIs Postman SQL Functional Testing Regression Testing AI Testing LLM Validation Agentic AI Prompt Validation Data Mapping Schema Validation Data Transformation Business Rule Validation Guardrail Testing Root Cause Analysis JIRA Agile Scrum Defect Management Analytical Thinking Communication Skills Expected Salary Range ChiStats has not officially disclosed compensation. Based on current AI QA hiring trends: 1–3 Years Experience: ₹8 LPA – ₹14 LPA Candidates with prior AI testing, migration testing, or strong API testing experience may receive higher offers depending on technical expertise. Expected Interview Rounds Since this is an AI-focused QA role, the interview process will likely evaluate both QA fundamentals and analytical reasoning. Round 1 – Resume Screening Recruiters generally assess: QA experience. API Testing projects. Data validation exposure. Communication skills. Round 2 – Face-to-Face Technical Interview Expect discussions on: API Testing JSON Validation Postman SQL Queries Migration Testing Data Mapping Integration Testing Regression Testing Defect Isolation STLC Agile Methodology Round 3 – AI & Data Validation Discussion Likely topics include: AI-generated outputs. Hallucination detection. Validation strategies. Agent workflows. Business rules. Data transformation. Guardrail validation. Non-deterministic AI responses. Prompt testing concepts. Round 4 – Managerial Interview Focus areas: Collaboration with US stakeholders. Communication skills. Problem-solving approach. Ownership. Investigation techniques. Round 5 – HR Discussion Topics generally include: Compensation. Joining timeline. Work location. Documentation. Preparation Tips Since this role combines traditional QA with AI validation, your preparation should be broader than standard testing interviews. Practice validating complex JSON payloads. Learn source-to-target migration validation techniques. Revise SQL joins and aggregation queries. Build Postman collections with automated validations. Read about AI hallucinations and guardrail concepts. Understand how AI agents consume structured data. Practice identifying incorrect AI outputs. Learn schema validation basics. Study data mapping and transformation scenarios. Prepare examples where you isolated complex production defects. Understanding why AI produced an incorrect output is often more valuable than simply identifying the defect. Resume Tips To maximize your shortlist chances: Highlight API Testing projects first. Mention Migration Testing experience. Showcase Data Validation work. Include SQL expertise. Add AI-related projects if available. Mention Integration Testing separately. Use measurable achievements. Keep the resume ATS-friendly. Recommended keywords: AI Testing Agentic AI API Testing Postman SQL Data Validation Migration Testing Integration Testing JSON Regression Testing Functional Testing Automation Testing Manual Testing Quality Assurance Data Integrity Defect Analysis