👋 Hi There!

I'M SUKESH S T

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Specializing in Generative AI, Agentic AI, and Machine Learning — building production-ready intelligent systems with LLMs, LangGraph, RAG, and modern AI frameworks.

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My Projects

Here's what I've been building — from multi-agent AI systems to real-time computer vision

Enterprise Research Agent

Multi-Agent AI Research Pipeline

The Problem

Enterprise teams need comprehensive research reports, but manually gathering, analyzing, and synthesizing information from multiple sources is extremely time-consuming and inconsistent.

The Goal

Build an intelligent, multi-agent AI system that automates the entire research pipeline — from planning queries to generating polished, cited reports — with human oversight at critical decision points.

How I Built It

Engineered a stateful, multi-agent pipeline using LangGraph, orchestrating Planner, Researcher, Extractor, Writer, and Reviewer agents. Implemented Human-in-the-Loop (HITL) checkpointing for dynamic re-routing. Developed concurrent async tool-calling across Tavily, Arxiv, and Wikipedia. Deployed on Hugging Face Spaces via FastAPI, SQLite, and Docker.

The Impact

Delivered a fully autonomous research system that generates comprehensive, multi-source reports with source attribution. Human-in-the-loop feedback ensures research quality while reducing manual effort by 80%+.

LangGraphLangChainFastAPIPythonSQLiteDockerTavilyArxiv

Flex Policies RAG Chatbot

Retrieval-Augmented Generation System

The Problem

Employees at Flex struggled to quickly find relevant information across hundreds of corporate policy documents, leading to compliance risks and wasted time.

The Goal

Create an AI-powered chatbot that enables natural language querying of corporate policy documents with accurate, source-attributed responses in real time.

How I Built It

Built a RAG pipeline using LangChain, ChromaDB, and SentenceTransformers for semantic search across policy documents. Developed a ChatGPT-style web interface with FastAPI backend, real-time similarity search, and markdown-rendered responses. Deployed as a Dockerized app on Hugging Face Spaces with Groq-hosted LLaMA 3.1 for low-latency inference.

The Impact

Created a production-ready chatbot with sub-second response times, source document attribution, and 95%+ answer relevance. Reduced policy lookup time from minutes to seconds.

LangChainChromaDBSentenceTransformersFastAPILLaMA 3.1GroqDockerPython

Scratch & Dent Detection System

AI-Powered Visual Quality Control

The Problem

At Flex Pvt Ltd, manual visual inspection of manufactured parts for scratches and dents was slow, inconsistent, and costly — leading to quality control bottlenecks on the production line.

The Goal

Develop an automated defect detection system that identifies scratches and dents in real-time using computer vision, reducing dependence on manual inspection.

How I Built It

Built a detection system using YOLOv11, Python, and OpenCV. Trained custom object detection models on labeled defect datasets. Implemented real-time image processing pipelines for continuous quality monitoring on the factory floor.

The Impact

Reduced manual inspection time by 40% via AI-driven quality control. Achieved real-time defect identification with high precision, enabling faster production throughput.

YOLOv11PythonOpenCVMachine LearningComputer Vision

Face Authentication System

🏆 1st Place — Internal Smart India Hackathon

The Problem

Traditional password-based authentication systems are vulnerable to phishing, brute-force attacks, and credential theft. Organizations needed a more secure, frictionless authentication method.

The Goal

Design and build a secure facial authentication system that provides reliable identity verification with anti-spoofing capabilities for the Internal Smart India Hackathon.

How I Built It

Designed a multi-layer facial authentication pipeline using Python, YOLO for face detection, and OpenCV for feature extraction. Implemented advanced recognition algorithms with liveness detection to prevent spoofing attacks.

The Impact

Won 1st Place at the Internal Smart India Hackathon. Delivered a robust authentication system with high accuracy and real-time performance suitable for enterprise deployment.

PythonYOLOOpenCVMachine LearningDeep Learning

Know Who I Am

A passionate AI/ML Engineer from Chennai, India

I'm Sukesh S T, an AI/ML Engineer specializing in Generative AI, Agentic AI, and Machine Learning. Currently pursuing my B.E. in Computer Science at Vel Tech Engineering College, Chennai with a CGPA of 8.15/10.

I'm passionate about building production-ready intelligent systems using LLMs, LangGraph, RAG, and modern AI frameworks. From designing multi-agent research pipelines to real-time defect detection systems, I thrive on turning cutting-edge AI research into scalable, real-world solutions.

When I'm not coding, I enjoy exploring new AI research papers, contributing to open-source projects, and solving problems on LeetCode.

B.E. Computer Science

Vel Tech Engineering College

2022 — 2026 | CGPA: 8.15

ML Intern — Flex Pvt Ltd

YOLOv11 Defect Detection

Jan 2025

1st Place — Internal SIH

Face Authentication System

2024

What I Do

Generative AI & LLMs

Building intelligent applications powered by Large Language Models, prompt engineering, and fine-tuning for domain-specific use cases.

Agentic AI Systems

Designing multi-agent pipelines with LangGraph — coordinating Planner, Researcher, and Writer agents with human-in-the-loop workflows.

Computer Vision & ML

Developing real-time detection and recognition systems using YOLOv11, OpenCV, and classical machine learning algorithms.

Full-Stack AI Applications

End-to-end AI product development with FastAPI backends, modern web frontends, Docker deployments, and cloud infrastructure.

Tech Stack

Languages

Python
Java
C++
JavaScript

AI / ML

PyTorch
LangChain
LangGraph
RAG
OpenCV
YOLOv11
NumPy
Pandas
Machine Learning
SentenceTransformers

Databases

MySQL
ChromaDB
FAISS

Web & APIs

FastAPI
HTML
CSS

Tools & Platforms

Docker
Hugging Face
Git

CRM

Salesforce

My Resume

Education, experience, and qualifications

Career Objective

AI/ML Engineer specializing in Generative AI, Agentic AI, and full-stack AI applications. Experienced in building production-ready intelligent systems using LLMs, LangGraph, RAG, and modern AI frameworks, with a strong focus on scalable, real-world solutions.

Education

Bachelor of Engineering in Computer Science

CGPA: 8.15/10

Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai

2022 — 2026

Relevant Coursework: Data Structures, Algorithms, Machine Learning, DBMS, Networks, OOAD

Higher Secondary Education (HSC)

74.83%

SRKBVMHSS, Kulasekharam, Kanniyakumari

2022

Internship

Machine Learning Intern @ Flex Pvt Ltd

6 Jan — 20 Jan 2025

GitHub
  • Built Scratch and Dent Detection System using YOLOv11, Python, and OpenCV
  • Reduced manual inspection time by 40% via AI-driven quality control
  • Implemented real-time image processing for defect identification

Certifications

Salesforce Platform App Builder

Simplilearn

Aug 2025

AWS Certification

Prepsinsta

2025

Matlab Onramp

MathWorks

2024