ckhire,Chaitanya Khire,github

👋 Hi

With more than 8 years of professional experience I have expertise in deploying AI Models for Large Scale VMS Products providing real-time analytics and deployed over Server, Cloud and Edge
Thanks to my previous working on SQL Optimzer with proprietary contribution in Tree Parsing, Backpropagation and Tree Optimization Algorithm I precieve Neural Network functioning as different Tree Algorithms
🤠 I had lot of fun and learning while working intensively on Neural Network model parsing, converting to various frameworks, creating optimized files, implementing optimized network definition
💦 Last few years gave me an opportunity to contribute in every stage of AI-pipeline from data-collection to AI model deployment and creating products and libraries around AI models
🧠 Few hrs of week I spend in experimenting with new NN Architecture, playing with Optimiser Algorithm, trying different training strategies.
🧩 Currently I am actively working on extracting unique features from SSD without having additonal training
👷 I am open to contributing in AI it may be designing neural networks, formulating training strategies or writing new products for AI or with AI
📫 You can reach me on: [email protected]

devchait.github.io

Expertise:

• Effective Problem Solving
• Leading developers to extract there best
• Architecting the VMS Product suite with real-time Analytics
• Researching and selecting efficient tools for creating faster cost-effective AI/ML solutions
• Designing Frameworks from scratch to leading them to full-fledge working suite
• Selecting and formulating easy to contribute framework giving rise to automatic creation of new Products and automating existing workflows without explicit requirement for automation
• Graph parsing algorithm, Tree Optimization Algorithm, Backtracking learning Algorithm
• Working with PyTorch deep learning framework for design and implementation of CNN's for image recognition, classification, retrieval problems
• Deploying NN models to highly optimized IE's including NVIDIA TensorRT, Intel Openvino, TFlite and Android NDK's
• Optimizing end-to-end inference pipeline, Desigining new inferencing strategies for having larger throughput