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Nishad Kane
Mid-level Data Scientist & AI Engineer specializing in RAG, agentic AI, and production ML
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About
AI/data engineer who built a production LLM-powered schema drift detection system (LangChain/LangGraph) to catch semantic data changes before they break downstream analytics/ML. Deployed on AWS with Docker/S3 and implemented an LLM-as-a-judge evaluation framework to improve trust, reduce hallucinations, and control false positives/alert fatigue. Collaborated with non-technical risk/business analytics stakeholders at EY by delivering human-readable drift explanations that improved confidence in financial analytics dashboards.
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Key Strengths
- Built and deployed an LLM-powered schema drift detection system for production data pipelines
- Hybrid approach combining deterministic checks with LLM semantic analysis to reduce false positives and alert fatigue
- Designed measurable evaluation for LLM workflows using LLM-as-a-judge with accuracy/false-positive/latency/cost metrics
- Production deployment experience on AWS with Docker and S3-backed schema storage plus scheduled monitoring
- Strong orchestration experience for LLM workflows (LangGraph multi-step/agentic control) and data workflows (Airflow)
- Effective translation of technical data reliability issues into business-friendly explanations for non-technical stakeholders
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