Open to opportunities

Kamal Dhungana

@kamaldhungana

Lead Generative AI Engineer building governed agentic AI, RAG systems, and LLM evaluation pipelines for enterprise impact.

United States

Message

What I'm looking for

I’m looking for a role where I can own the full SDLC for enterprise agentic AI and RAG, build robust LLM evaluation and observability, and help deliver governed, secure deployments on GCP/Azure/AWS with strong stakeholder collaboration.

I’m a Lead Generative AI Engineer and Sr. AI consultant with 8+ years across generative AI, data science, machine learning, software development, and cloud analytics. I focus on designing and productionizing enterprise-grade agentic AI applications, RAG systems, and LLM-powered analytics end-to-end.

I bring strong hands-on depth in LangGraph, LangChain, Google ADK, Azure OpenAI, and Gemini/Vertex AI, paired with governed AI practices like Responsible AI, AI governance, PII masking, prompt injection defense, jailbreak testing, audit logging, and access control. I’ve built evaluation pipelines for chatbot quality, including regression testing, drift monitoring, and performance tracking, so teams can ship reliably.

Across roles, I’ve delivered production systems—from secure BigQuery query workflows and document intelligence workflows to memory-enabled agents (Firestore) and ETL pipelines (GCP Airflow) that produce agent-ready data. I value stakeholder leadership, SDLC ownership, and translating requirements into production-ready releases with onsite/offshore teams.

Experience

Work history, roles, and key accomplishments

Current

Sr. GenAI Developer

Current

NRG

Apr 2025 - Present (1 year 2 months)

Designed and delivered enterprise-grade GenAI solutions end-to-end, covering POC scoping, testing (unit/smoke/load), vulnerability review, CI/CD, and production deployment. Built agentic analytics with Google ADK to query BigQuery via natural language, added Firestore memory, and implemented RAG and evaluation pipelines including drift monitoring.

Generative AI Agentic AI RAG LLM Evaluation Google ADK BigQuery Firestore CI CD Airflow Docker

Sr. GenAI Developer

KPMG

Mar 2024 - Apr 2025 (1 year 1 month)

Implemented GenAI solutions for audit and compliance workflows using Azure OpenAI, Azure Document Intelligence, and LangGraph. Built agentic document workflows and a text-to-SQL agent proof of concept, improving reliability with reflection-based feedback loops and few-shot prompting while using LangSmith for evaluation.

Azure OpenAI Azure Document Intelligence LangGraph Agentic AI Text To SQL LLM Evaluation CI CD CI Pipelines Prompt Engineering

Sr. Data Scientist

Intent

Jul 2020 - Mar 2024 (3 years 8 months)

Containerized and deployed a RAG chatbot using Docker, FastAPI, LangChain, OpenAI LLMs, Pinecone, and MongoDB for targeted interactions. Built an LLM-powered CSV data agent, developed audio/video summarization workflows, and trained ML models with AWS SageMaker and REST APIs via AWS Lambda and API Gateway.

RAG Bots Lang Chain Pinecone fastAPI Docker AWS Sagemaker AWS Lambda API Development

Data Scientist

Monsanto / Bayer

Oct 2018 - Jul 2020 (1 year 9 months)

Built production models to predict corn flowering, silking, and harvest dates across North and South America using linear mixed models and random forests. Implemented Kriging/co-Kriging/IDW interpolation workflows and automated daily predictive pipelines using PostgreSQL, AWS, Python/R, Linux/Domino, and S3.

Random Forest Kriging PostgreSQL AWS Python R Data Pipelines

Data Scientist

Express Scripts

Feb 2018 - Sep 2018 (7 months)

Developed forecasting models for agile story completion, cycle time, and velocity using regression, classification, random forests, gradient boosting, neural networks, and time-series methods. Performed feature engineering and T-SQL extraction, and created Tableau reporting and Jira story classification insights to guide team improvements.

regression Classification Random Forest Gradient Boosting Neural Networks Time Series Modeling Feature Engineering T SQL Tableau Machine Learning

Postdoctoral Scholar

Florida State University / University of Iowa

May 2015 - Jan 2018 (2 years 8 months)

Developed Azure Machine Learning web-service predictive models using boosted decision trees, linear regression, and Bayesian linear regression. Built R modeling APIs and 15+ Tableau dashboards for customer analytics, segmentation, recommendation, and KPI tracking, while communicating results to diverse audiences.

Azure Machine Learning Bayesian Linear Regression Tableau Statistical Modeling Customer Analytics Data Visualization

Education

Degrees, certifications, and relevant coursework

Michigan Technological University

Doctor of Philosophy, Computational Physics

Ph.D. in Computational Physics completed in 2015, focused on numerical modeling, Quantum Monte Carlo simulations, and nanoscale device simulation using Python/MATLAB/Fortran.