Vetted Apache Hive Professionals

“LLM/AI engineer who has deployed production RAG conversational analytics and Text-to-SQL systems over Snowflake and curated data marts, emphasizing enterprise-grade guardrails for accuracy, security, and cost. Notable for a structured approach to reducing hallucinations (curated metric/table registry, SQL validation, RBAC, and citation-backed responses) and for building resilient, observable multi-step agent workflows using LangChain/LlamaIndex and Airflow.”

“Built a production-grade LLM evaluation and regression system that stress-tests models across hundreds of iterations, combining LLM-as-judge, semantic similarity, statistical metrics, and rule-based checks, with results delivered via stakeholder-friendly HTML reports and dashboards. Experienced orchestrating multi-agent RAG workflows using LangChain/LangGraph and event-driven GenAI pipelines in n8n integrating OCR, speech-to-text, and external APIs, with strong emphasis on reliability, observability, and explainable failures.”

“Data scientist who has built production LLM agents (GPT-4o + LangChain + RAG) to automate analyst-style ad hoc CSV analysis with guardrails and GPT-as-a-judge evaluation. Also delivered an explainable healthcare NLP system for ICD code classification by collaborating closely with clinicians, using a hybrid rule-based decision tree + BERT model to reach 97% accuracy and cut manual review time.”

“LLM/agent systems builder who deployed a production hybrid router for immerso.ai that dynamically selects retrieval vs reasoning vs generative pathways, achieving an 82% factual-accuracy lift. Deep hands-on experience optimizing local Mistral 7B inference (4–5 bit GGUF quantization, KV-cache reuse) and building reliable RAG/agent workflows with LangChain/LangGraph/AutoGen across GCP Cloud Run and AWS (ECS/Lambda).”

“Built a production-style AI meeting summarization and action-item extraction system (Azure Speech-to-Text + transformer summarization/NER) exposed via a Flask REST API, with explicit guardrails to prevent hallucinated tasks. Strong focus on reliability: modular agent/workflow design, precision-first evaluation with human-validated golden notes, and practical orchestration patterns (tool-augmented agents; ready to scale into Airflow/LangGraph/Prefect).”