Generative AI & Agentic AI builder — RAG, multi-agent systems, fine-tuning, and applied comparisons across frameworks — with a data analyst foundation underneath.
I build with Generative AI — retrieval-augmented generation, agentic systems, and parameter-efficient fine-tuning. My background is in Information Systems (M.S., Indiana Institute of Technology), which gave me an entry point into NLP through the field's older questions of how knowledge is organized and made findable, rather than through model architecture first. I learn by building, comparing, and reporting what actually happened — several of my projects deliberately implement the same task two ways, specifically to surface real differences rather than assume one approach represents a whole technique. Alongside GenAI work, I have a solid foundation in data analysis, Python, SQL, and machine learning.
Full Stack Data Science with GenAI & Agentic AI
Naresh i Technologies
Hands-on training in data analysis, machine learning, LLMs, LangChain/LangGraph, RAG, fine-tuning, and agentic AI application development.
Master of Science — Information Systems
Indiana Institute of Technology, Fort Wayne, USA
Aug 2023 – May 2025
Focus areas: Data Management, Cloud Computing, System Architecture.
B.Tech — Computer Science & Engineering
JNTUH, India
2018
Foundation in algorithms, databases, object-oriented programming, and software engineering principles.
Five projects below, each demonstrating a genuinely distinct GenAI mechanism. Several are deliberate comparisons — two frameworks, two QA approaches — built to surface real findings rather than a single success story.
A retrieval-augmented Q&A system answering questions about six foundational RAG papers, grounded in the papers' own text rather than the model's general knowledge — using the method to study the method.
The same two-agent research-and-summarize task built in two different agent frameworks, compared side by side in a Streamlit app — to isolate whether framework choice or tool choice actually drives the difference in output, rather than assuming one implementation represents "agent frameworks" generally.
An agent that decides which Pandas operations to run to answer natural-language questions about a retail sales dataset, tested specifically on multi-step questions — filter, then group, then aggregate, including a nested per-group "top item in each category" query.
Fine-tuned distilgpt2 with LoRA to consistently follow a fixed answer format, training ~0.36% of the model's parameters. Evaluated with a genuine before/after comparison, including questions never seen during training.
A controlled comparison of extractive QA (a BERT-family model that can only point at existing text spans) against generative QA (LLM, grounded via prompting) on the same source text and questions.
The same retrieval pipeline as the papers project, generalized to arbitrary, user-uploaded documents rather than a fixed, curated knowledge base.
A controlled test of negative prompting and style-modifier keywords on Stable Diffusion, holding the subject constant. Negative prompting produced a clear, visible improvement; style keywords showed little effect on this endpoint — a real, somewhat counterintuitive finding.
A deliberate contrast to the GenAI projects above: TF-IDF + Naive Bayes, included to demonstrate that not every NLP problem needs an LLM. Evaluated with precision/recall rather than accuracy alone, given an 87/13 class imbalance.
A complete end-to-end Data Analytics + Machine Learning project analysing 1,300+ healthcare insurance records to identify key cost drivers and predict medical charges, from raw CSV to deployed Streamlit web app.
Full Stack Data Science & GenAI Trainee
Naresh i Technologies
Dec 2025 – Present
Software Engineer
Cortracker Inc
Feb 2025 – Nov 2025