Functional AI Tester

Michelin; Pune District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Michelin in Pune, India seeks a Quality Assurance (QA) Engineer focused on testing Generative AI applications with Python-based test automation. You will design end-to-end test strategies for GenAI features, including RAG, conversational AI and agentic evaluations, ensuring accuracy, reliability and safety.

You will work hands-on with AI and data engineers to define acceptance criteria, build automated tests with PyTest, implement data-quality validation for ETL pipelines, and develop evaluation

Qualifications

• 5+ years in software QA, including test strategy, automation, and defect management.
• 2+ years testing AI/ML or GenAI features, with hands-on evaluation design.
• 4+ years testing ETL/data pipelines and data quality.

Responsibilities

• Test strategy and planning for GenAI features with risk-based approaches.
• RAG and semantic retrieval testing including grounding quality and hallucination checks.
• API and application testing for REST endpoints supporting GenAI features.

Skills

Python automation

GenAI evaluation

ETL testing

Test strategy

PyTest

Dataset management

Prompt validation

Golden datasets

Quality metrics

Tools

PyTest

Job description

We are seeking a Quality Assurance (QA) Engineer focused on testing Generative AI (GenAI) applications with a strong emphasis on Python-based test automation, GenAI evaluation, and ETL/data quality validation. You will design and execute end-to-end test strategies

that ensure our AI solutions are accurate, reliable, safe, and compliant.

About the Role

You will be involved in QA for GenAI features including Retrieval-Augmented Generation (RAG), conversational AI and Agentic evaluations.

The role centers on:

• Systematic GenAI evaluation (qualitative and quantitative metrics)
• ETL and data quality testing for the data flows that feed AI systems
• Python-driven automated testing

This position is hands-on and collaborative, partnering with AI engineers, data engineers, and product teams to define measurable acceptance criteria and ship high-quality AI features.

Key Responsibilities

• Test strategy and planning
  • Define risk-based test strategies and detailed test plans for GenAI features.
  • Establish clear acceptance criteria with stakeholders for functional, safety, and data quality aspects.
  • Build and maintain automated test suites using Python (e.g., PyTest, requests).
  • Implement reusable utilities for prompt/response validation, dataset management, and result scoring.
  • Create regression baselines and golden test sets to detect quality drift.
  • Develop evaluation harnesses covering factuality, coherence, helpfulness, safety, bias, and toxicity etc.
  • Design prompt suites, scenario-based tests, and golden datasets for reproducible measurements.
  • Implement guardrail tests including prompt-injection resilience, unsafe content detection, and PII redaction checks.
  • Track quality metrics over time.
• RAG and semantic retrieval testing
  • Verify alignment between retrieved sources and generated answers.
  • Verify adversarial tests.
  • Measure retrieval relevance, precision/recall, grounding quality, and hallucination reduction.
• API and application testing
  • Test REST endpoints supporting GenAI features (request/response contracts, error handling, timeouts).
• ETL and data quality validation
  • Test ingestion and transformation logic; validate schema, constraints, and field-level rules.
  • Implement data profiling, reconciliation between sources and targets, and lineage checks.
  • Verify data privacy controls, masking, and retention policies across pipelines.
  • Performance and load testing focused on latency, throughput, concurrency, and rate limits for LLM calls.
  • Cost-aware testing (token usage, caching effectiveness) and timeout/retry behavior validation.
  • Reliability and resilience checks including error recovery and fallback behavior.
• Quality and governance
  • Understanding of LLM limitations and methods to detect/reduce hallucinations.
  • Safety and compliance testing including PII handling and prompt-injection resilience.
  • Strong analytical and debugging skills across services and data flows.
  • Excellent written and verbal communication; ability to translate quality goals into measurable criteria.
  • Collaboration with AI engineers, data engineers, and product stakeholders.
  • Organized, detail-oriented, and outcomes-focused.
• Share results and insights; recommend remediation and preventive actions.

Required Qualifications

Experience

• 5+ years in software QA, including test strategy, automation, and defect management.
• 2+ years testing AI/ML or GenAI features, with hands-on evaluation design.
• 4+ years testing ETL/data pipelines and data quality.

Technical skills

• Python: Strong proficiency building automated tests and tooling (PyTest, requests, pydantic or similar).
• GenAI evaluation: crafting prompt suites, golden datasets, rubric-based scoring, and automated evaluation
• RAG testing: retrieval relevance, grounding validation, chunking/indexing verification, and embedding checks.
• ETL/data quality: schema and constraint validation, reconciliation, lineage awareness, data profiling.

Nice to Have

• Experience with evaluation frameworks or tooling for LLMs and RAG quality measurement.
• Experience creating synthetic datasets to stress specific behaviors