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.
