The engineer who crossed disciplines.
Mechanical engineer turned AI/ML researcher. I train models, build pipelines, and ship products. Not the “I use the ChatGPT API” kind.
I care about the gap between a model that works on Colab and one that holds up on real sensor data. That's where most of my work lives.
Impact by the numbers
Currently exploring
Reinforcement learning, machine vision, and AI-driven industrial automation. Also building production-grade generative AI apps, not wrappers. Full systems.
“Mechanical engineering taught me how systems fail. AI taught me how to predict it before they do.”
Where I've built things.
John Deere India Pvt. Ltd.
AI/ML Intern
- →Applied object detection & segmentation models (YOLO, SAM2, DETR, ViT, Grounding DINO) to automate manual processessaved 4+ hrs/task
- →Fine-tuned Large Language Models on custom image datasets50% accuracy improvement, hallucinations reduced
- →Built end-to-end speech-to-text pipeline: Whisper + GPT-4o Transcribe + GPT-4o Mini with JS frontend/backend10% transcription boost, saved 2 hrs/day
- →Designed multi-agent workflow using Knowledge Graphs + GNN, deployed on AWS (EC2, S3, DynamoDB)4+ hours saved in document workflows
- →Implemented cross-encoder reranker for semantic retrieval outperforming similarity search. Databricks for large-scale extraction + OpenAI embeddings for evaluation
- →Conducted hallucination-reduction experiments for deployed LLMsimproved reliability & robustness
K.S.J Recruitment Pvt. Ltd.
Data Analyst Intern
- →Created and maintained Power BI dashboards for recruitment metrics, providing actionable insights to optimize hiring strategies
- →Performed data cleaning and analysis using Excel and Python ensuring accurate reporting
- →Automated data workflows20% reduction in manual effort
- →Collaborated with cross-functional teams to streamline recruitment data pipelines
Here's what I've been building.
Flagship project
Agentic Industrial Diagnostics
“What if a maintenance engineer could sleep while four AI agents diagnosed the breakdown?”
Multi-agent LLM system for real-time root cause analysis of industrial equipment failures. An LSTM Autoencoder catches anomalies with 95.3% precision, an OWL/RDF knowledge graph maps causal chains, and four LangGraph agents (Diagnostic, Reasoning, Planning, Learning) collaborate to produce an explanation and remediation plan in ~77 seconds.
FastAPI · LangGraph · LangChain · Gemini · LSTM · OWL/RDF · Next.js 14
More work
- 01
SwishFit
LiveNov 2025AI-powered basketball training platform where Google Gemini 2.5 Flash generates personalised workout plans. Role-based access for players, coaches, and admins. JWT + bcrypt + Helmet.js security stack. Deployed on Vercel + Render + MongoDB Atlas.
React (Vite) · Node.js · Express · MongoDB Atlas · Gemini 2.5 · JWT
- 02
GangaFlow
IEEE ICoICC 2025Dec 2024AI-driven water quality monitoring for the Ganga River Basin. YOLOv8 bounding box detection, U-Net area segmentation, and AlexNet severity classification fused into a single real-time pipeline giving environmental agencies actionable, annotated outputs.
Python · YOLOv8 · U-Net · AlexNet · OpenCV
- 03
CNC Anomaly Detector
Jan 2025Convolutional autoencoder trained on tri-axial vibration signals from CNC milling machines. Dynamic threshold adapts to machine state drift without retraining. Ships with a live Streamlit dashboard: upload a CSV, get a verdict.
- 04
Mild Steel Degradation
1st Runner-up, Ninja Hack 2K252025Real-time corrosion detection on microscopic steel imagery using CNN + HSV colour segmentation + Canny edge detection. Flask web app with corrosion zone heatmap overlays and automated severity report generation.
Python · CNN · OpenCV · Flask
- 05
Hotel Reservation MLOps
Apr 2025End-to-end MLOps workflow: MLflow tracks every experiment, FastAPI serves predictions behind API key auth. Ships in Docker Compose.
Python · MLflow · FastAPI · Random Forest · Docker · scikit-learn
Research contributions.
GangaFlow: A Multi-Model Deep Learning Framework for Real-Time River Pollution Detection and Analysis Using Drone Imagery
Agentic Industrial Diagnostics: A Multi-Agent LLM Framework for Explainable Root Cause Analysis
Outside the notebooks.
1st Runner-up
The Great Ninja Hack 2K25
DYPCET Kolhapur
Mild Steel Degradation Analysis Using Microscopic Imaging & Deep Learning
1st Position
Smart India Hackathon
Internal, SIT
Traffic Signal Control System using YOLOv5 + Reinforcement Learning
2nd Runner-up
Best Manager Competition
NICMAR University
Competed in the prestigious national-level management challenge
Zonal Champion
Basketball, Team Captain
Deogiri Institute
Led college team to zonal championship. Competed at State & National level.
Not everything I build is code.
What people say.
From colleagues, managers, and mentors, in their own words.
“I guided Omkar through his M.Tech dissertation project over the past year, with a focus on agentic AI for predictive maintenance closely aligned with my own research area. What stood out was his ability to work independently. He took ownership of both the research and implementation, showing strong research instincts and a genuinely hardworking attitude. Our collaboration resulted in a paper currently under review with Springer. He has deep knowledge of agentic frameworks and systems, and the kind of intellectual curiosity that drives good research. I'd genuinely recommend him for roles in AI/ML, he's the kind of student you don't have to push.”
“I had the chance to work closely with Omkar during our M.Tech, where we collaborated on projects, patents, and hackathons together. He's a dedicated and skilled person who always brings good ideas and a calm problem-solving approach to the team. It was great continuing the journey as colleagues at John Deere as well. I'd definitely recommend him to anyone looking for a reliable and talented professional.”
“I had the privilege of collaborating with Omkar during his internship, where he contributed to several AI and automation projects. His energy and eagerness to learn were remarkable, especially in exploring and implementing GenAI technologies and making them practically workable. As an intern, grasping complex concepts and applying them effectively is never easy, yet Omkar managed it with confidence. Though he started with limited business knowledge, he quickly picked up the essentials and worked towards delivering solutions aligned with business needs. Omkar's adaptability, curiosity, and commitment to learning made him a valuable contributor, and I am confident he will continue to excel in his career.”
Latheef D
Data Scientist · John Deere
Project lead on the 2D drawing automation tool
Full recommendations on LinkedIn
Where the foundation was built.
M.Tech in Artificial Intelligence and Machine Learning
Symbiosis Institute of Technology
2024 – 2026
Coursework
B.Tech in Mechanical Engineering
Deogiri Institute of Engineering and Management Studies, Aurangabad
2020 – 2023
Coursework
Certifications
Generative AI Engineering with LLMs
IBM
Data Analysis and Visualization with Power BI
Microsoft
AWS Cloud Training
Unnati Development Pvt Ltd
Things worth writing down.
I write about what I'm learning: RAG pipelines, ML systems, things that took me too long to figure out.
Getting Started with RAG: Building a Document Q&A System
How to build a retrieval-augmented generation pipeline from scratch: chunking, embeddings, and why similarity search isn't always enough.
What I Learned Building a Multi-Agent System at John Deere
The gap between a multi-agent demo and a production-grade system is larger than the papers make it seem. Here's what actually surprised me.
From Mechanical Engineering to ML: The Honest Version
Not a LinkedIn pivot story. A practical account of what transferred, what didn't, and what I had to relearn from scratch.
Let's build something.
I'm open to ML engineering roles, research collaborations, and genuinely interesting problems. Drop me a line. I read every message.