AI ML Validation Engineer

AI Validation & Testing

Chatbot & RAG Testing  •  LLM/Prompt Evaluation  •  Python Validation

• AI Solution Testing: Validates AI-powered solutions including chatbots and generative AI features for accuracy, reliability, and edge cases
• RAG Validation: Tests Retrieval-Augmented Generation pipelines for retrieval relevance, grounding, and hallucination handling
• Agentic AI: Builds and tests agentic solutions such as MCP and prompting

Test Data & AI Evaluation

Data Quality  •  AI Eval Metrics  •  Responsible AI

• Test Data Management: Curates and versions golden/evaluation datasets and validates data pipelines for integrity, completeness, and drift across training and inference
• AI Evaluation: Hands-on with evaluation frameworks and metrics such as RAGAS, DeepEval, and LLM-as-judge, covering groundedness, relevance, and precision/recall
• Responsible AI & Privacy: Bias and fairness checks, safety/guardrail, and PII/data-leakage validation

Languages & Frameworks

Python  •  Pytest

• Core Languages: Proficient in Python for automation
• Test Frameworks: Hands-on with Pytest, or equivalent
• Framework Design: Build, scale, and maintain automation frameworks end-to-end

Secondary Skill

UI & API Testing

UI Automation  •  REST/GraphQL APIs  •  Contract Testing

• UI Automation: End-to-end browser testing across modern web applications. Well versed with BDD frameworks like Cucumber/SpecFlow
• API Testing: Validates REST/GraphQL APIs across microservices for correctness and edge cases
• Cross-layer Coverage: Bridges UI and API layers for integrated test coverage
• Tools: Experience with tools like Postman/Insomnia/Newman

Cloud, Infrastructure & CI/CD

AWS  •  SQL  •  CI/CD Pipelines

• Cloud Exposure: Working knowledge of AWS/Azure services for test environments
• SQL Proficiency: Uses SQL to query, validate, and debug data confidently
  • CI/CD Integration: Integrates test suites into Jenkins, GitHub Actions, GitLab CI, etc.