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Amit Gangane
Junior Data Scientist specializing in agentic AI and RAG pipelines
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About
LLM/agentic systems builder who shipped production workflows at Angel Flight West and Eureka AI, combining LangGraph + RAG (Postgres/pgvector) with strong observability (LangSmith/Langfuse). Delivered large operational gains (address lookup cut from 10 minutes to 60 seconds; accuracy to 92%) and has a track record of quickly stabilizing customer-critical pipelines (Pydantic-enforced JSON for ETL) while partnering with sales/ops to drive adoption.
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Key Strengths
- Took an LLM agent from prototype to production with measurable impact (10 min to 60 sec lookup; 30% to 92% accuracy)
- Systematic LLM debugging using observability/tracing (LangSmith/Langfuse) to pinpoint failures
- Improved extraction precision by 23% via prompt iteration and few-shot examples for messy real-world data
- Improved retrieval performance by tuning embeddings/chunking (reported 95% retrieval accuracy)
- Rapid incident response: fixed malformed JSON breaking ETL by enforcing Pydantic structured outputs (deployed within hours)
- Strong technical communication: practical developer-focused demos with code, architecture, and design tradeoffs
- Cross-functional partnership with sales/ops to align technical outputs to customer needs and drive adoption
- Built and deployed production agentic LLM workflow classifying 125K+ unstructured network traffic logs; reduced manual labeling work by 90%+
- Improved unknown-domain classification accuracy via RAG + dynamic search grounding (A/B test: 60% -> 89% on 1,000 logs)
- Enforced reliable structured outputs using Pydantic JSON schemas to eliminate formatting errors in downstream ETL
- Designed scalable cloud data architecture (BigQuery streaming + Cloud Functions) processing telco data across US/HK/UK
- Used LangSmith tracing/evaluation to identify failure modes and improve extraction accuracy by 23%
- Established measurable success metrics and built ground-truth labeled evaluation sets (e.g., manually verified 200 facilities) to track precision/recall
- Delivered AI solution with non-technical stakeholders by shadowing users and prioritizing transparency/verification; reduced lookup time from 10 minutes to 60 seconds with ~90% accuracy
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