- 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