sumukh g sumukh30

👋 About Me

sumukh= {
"currently" : "MS Computer Science @ University of Illinois Chicago (GPA: 4.0)",
"background" : "2+ years Full-Stack Engineering @ Oracle",
"focus" : ["LLM Fine-Tuning", "Agentic AI", "Computer Vision", "RAG Systems"],
"looking_for": "AI/ML Engineering | Full-Stack Engineering roles",
"location" : "Chicago, IL"
}

🔩 I bring production engineering discipline to AI/ML — having built and scaled enterprise systems before transitioning to building production-grade AI systems.
⚡ I don't just train models. I ship them.

🚀 Featured Projects

📚 LearnMate — Adaptive AI Study Copilot

Personal Project

An adaptive study copilot combining RAG and agentic orchestration — answers doubts from a student's own notes and generates quizzes that adjust difficulty based on tracked per-student performance, not a one-off interaction.

5-intent LangGraph pipeline:
Classify Intent → Retrieve → Generate → Log Result → Adapt Next Quiz

Highlights:

🧠 Doubt-answering grounded strictly in ingested notes, source-cited
📈 Quiz difficulty auto-adjusts to tracked weak topics
🛡️ Fixed silent LLM misclassification with deterministic fast-paths + write-guards

Stack: LangChain · LangGraph · Chroma · Ollama · FastAPI · React

🤖 DataTalk — NL-to-SQL BI Agent

Google Cloud Rapid Agent Hackathon 2026

A multi-step agentic system that lets non-technical users query business data in plain English — no SQL required. Built at the "Google Cloud Rapid Agent Hackathon 2026".

7-step reasoning pipeline:
Schema Introspection → 5 SQL Candidates → BigQuery Execution → Quality Ranking → Human Approval → Insight Generation → History Logging

Stack: Google ADK · Gemini 2.5 Flash · Vertex AI · BigQuery · Fivetran MCP · Firestore · Flask · Cloud Run

🔬 Parameter-Efficient Text-to-SQL

LLM Fine-Tuning Research

Fine-tuned Phi-3.5-mini (3.8B) using LoRA on the Spider benchmark — matching models 4× larger.

Results:

📈 52.5% execution accuracy
🚀 10× exact match improvement (3.5% → 37.5%)
⚡ <1% of parameters updated via LoRA

Stack: PyTorch · HuggingFace · LoRA/PEFT · Spider Benchmark

🌐 Oracle Enterprise Portal

Production — 13M+ Users

Full-stack web portal serving 13M+ users at Oracle — REST APIs, frontend systems, B2B data pipelines, and ML-powered sales lead scoring.

Highlights:

🏗️ REST API latency reduced 15–25%
⚙️ Groovy automation: 10–20s → instant
🤖 ML classification for sales lead conversion probability
📉 95% manual effort eliminated

Stack: HTML · CSS · JavaScript · REST APIs · Groovy · Oracle B2B Cloud

🛠️ Tech Stack

Languages

AI / ML

Cloud & MLOps

Web & Backend

🎓 Education & Experience


🎓 MS Computer Science — University of Illinois Chicago	`Aug 2025 – May 2027` · GPA: 4.0 / 4.0
🎓 BE Computer Science — Visvesvaraya Technological University	`Aug 2018 – Jul 2022` · GPA: 3.36 / 4.0
💼 Senior Cloud Analyst — Oracle	`Sep 2024 – Oct 2024`
💼 Cloud Analyst — Oracle	`Aug 2022 – Aug 2024`
💼 Software Development Intern — Phamax	`Dec 2021 – Mar 2022`

🧠 What I'm Working On

Learning by developing scalable, production-ready AI applications using modern AI, cloud, and software engineering tools to solve real-world problems
Targeting AI/ML Engineer and Full-Stack Engineer roles

Building things that matter - one commit at a time.

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sumukh g sumukh30

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