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Rohan Chhabria

@rohanchhabria

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AI/ML Engineer with 2.6+ years of experience in ML, Computer Vision, Gen AI, NLP.

India

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What I'm looking for

Looking for a challenging role that leverages my expertise in AI, Computer Vision, NLP and Generative AI to drive innovation and growth.

I am a highly skilled AI Consultant and Machine Learning Engineer with a strong background in Artificial Intelligence, Computer Vision, and Deep Learning. I hold a Master's degree in Computer Science with a concentration in Artificial Intelligence from HSNC University, and a Bachelor's degree in Computer Science from the University of Mumbai.

Throughout my career, I have developed and deployed advanced systems leveraging LLMs, RAG techniques, Computer Vision, and Custom pipelines. I have expertise in training and deploying deep learning models, including YOLO, ResNets, ConvNets, ViT, Transformers, BERT, Llama, GPT, and Flan-T5. I am proficient in a range of technologies, including Git, Google Colab, AWS, PostgreSQL, DVC, and Docker.

I have a proven track record of delivering high-accuracy results, including achieving a 95% accuracy rate for red blood cells, white blood cells, and platelets, and a 92.16% balanced accuracy score between multiple models. I am passionate about staying up-to-date with the latest advancements in AI and Machine Learning, and I am excited to bring my skills and expertise to a new role.

Experience

Work history, roles, and key accomplishments

AI Consultant

Stealth Startup

Dec 2023 - Jul 2024 (7 months)

Developed AI system for food label compliance using LLMs, RAG, and Computer Vision. Implemented YOLO-NAS for logo/nutrition detection (94% mAP). Created feature for keyword and visual element analysis, including artwork height validation. Leveraged OCR, CNN, embeddings, OpenCV, ONNX, Docker, Lambda, NumPy, and Pillow. System assesses any food label across all verticals, enhancing accuracy.

Python Django Flask SQL PyTorch OpenCV Docker AWS Lambda AWS S3 AWS EC2

Machine Learning Engineer (MLE)

Adsys

Nov 2021 - Nov 2022 (1 year)

Developed AI for CBC device: implemented image stitcher for blood slides, improved cell detection (20% precision increase), and classified blood cells (95% accuracy). Created pipeline for efficient high-res image inference. Trained YOLO models on Nvidia A10G, achieving 92.16% accuracy, 93.84% precision, 93.37% recall, 93.13% F1 score. Used CNN, OpenCV, PyTorch, SAHI, and S3.

Python PyTorch OpenCV Gold YOLO Project Management AWS Linux Machine Learning Data Science Remote Team Remote Work Data Visualization REST APIs Deep Learning AI Machine Learning Computer Vision

Machine Learning Engineer (MLE)

Paralaxiom Technologies

Oct 2021 - Oct 2022 (1 year)

Created service for 75% faster video frame deletion. Developed fire/smoke detection system using image processing and deep learning. Implemented scrub detection system (95% accuracy) for classifying medical staff. Optimized performance with quantization, reducing processing time by 70%. Trained YOLOv3 models (95% mAP) on Nvidia Tesla T4, automating process. Used CNN, OpenCV, TensorFlow, Nvidia TAO