Kamal Dhungana
Studied at Michigan Technological University
Worked at NRG Energy
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About Kamal
My career has been focused on turning complex data and AI concepts into practical business solutions. I started in core data science and analytics, where I worked on solving real-world problems using data, machine learning, and automation. Over time, my work evolved into Generative AI and agentic AI systems, where I now focus on designing, building, and deploying AI-powered solutions that can reason over data, retrieve knowledge, use tools, and support business decision-making. I have hands-on experience building end-to-end agentic solutions, including RAG systems, AI-powered analytics agents, call-center intelligence platforms, and natural-language interfaces for enterprise data. My work covers the full lifecycle: identifying the right business problem, building prototypes, integrating LLMs with structured and unstructured data, implementing memory, observability, traceability, evaluation, and human-in-the-loop workflows, and moving solutions into production. I enjoy helping others understand how to move beyond AI demos and build reliable, scalable AI systems that create real business value.
Why do I coach?
Clear guidance can make a big difference when someone is learning a complex field like AI, machine learning, or Generative AI. I have gone through the journey of moving from technical concepts to real-world implementation, and I understand that the hardest part is often not learning the theory, but knowing how to apply it to actual business problems. Coaching matters to me because I enjoy helping people gain clarity, confidence, and direction. My goal is to make AI feel practical and approachable, whether someone is trying to understand the fundamentals, build a prototype, design an agentic solution, or move an AI idea into production. I want clients to leave each session with clear next steps and the confidence to keep building.
Work Experience
Gen AI Product Lead
NRG Energy
April 2025 - June 2026
Designed and implemented end-to-end enterprise GenAI solutions at scale, from proof of concept and architecture design to testing, CI/CD, deployment, and production support. Built agentic AI solutions that allow business users to query enterprise data in natural language and receive insights, summaries, and KPI-driven analysis. Developed AI-powered ETL pipelines to process large volumes of audio files, generate transcripts, redact PII/PCI data, extract structured entities, and prepare data for downstream analytics agents. Implemented production-ready AI components such as memory, observability, traceability, evaluation pipelines, guardrails, and governed data access. Built multiple RAG and GraphRAG solutions using Vertex AI, vector search, LightRAG, and Neo4j to improve enterprise knowledge retrieval and grounded LLM responses.
AI Consultant
KPMG
March 2024 - April 2025
Designed and implemented GenAI solutions for audit and compliance workflows using Azure OpenAI, Azure Document Intelligence, LangGraph, and custom AI tools. Built agentic frameworks to classify documents, validate mathematical accuracy, support vouching, and improve audit procedure automation. Extracted and standardized information from PDFs, images, CSVs, and text files to improve document intelligence, consistency, and audit readiness. Integrated custom tools into LangGraph and ReAct-based workflows to support calculations, reasoning, tool execution, and decision support. Led delivery coordination across onsite and offshore teams by translating business requirements into technical tasks, Jira stories, and production-focused implementation plans.
Senior Data Scientist
INTENT
July 2020 - March 2024
Containerized and deployed a RAG chatbot with Docker, FastAPI, LangChain, OpenAI LLMs, Pinecone vector database, and MongoDB response storage for targeted farmer interactions. Created an LLM-powered CSV data agent to answer natural-language questions, extract insights from structured data, and generate contextual visualizations dynamically. Built NLP summarization applications for audio and video content using LLMs and prompt engineering to produce concise summaries from transcripts and media-derived text. Trained and deployed ML models with Amazon SageMaker and integrated scalable REST APIs using AWS Lambda and API Gateway. Developed computer vision models for crop and weed detection in drone videos using transfer learning and YOLO-v3. Applied clustering, anomaly detection, regression, hypothesis testing, and power analysis across weather, sensor, yield, and agronomic datasets; communicated findings to clients and management. Built interactive analytics products and dashboards with Python, hvPlot, Tableau, Power BI, Apache Superset, and geospatial data sources; partnered with DevOps teams to productionize ML/NLP applications.
Data Scientist
Bayer
October 2018 - July 2020
Education
Michigan Technological University
Ph.D, Computational Physics
2009 - 2015