Tech Stack: Python, Go, Lua, C, C++, Julia, Java, MySQL, JavaScript, TypeScript, Bash, Tensorflow, Keras, Pytorch, StencilJS, AG-Grid, Pandas, Numpy, Matplotlib, OpenCV, Flask, Git, Vim, Tmux, Docker, REST, NodeJS, Kubernetes, DynamoDB, Redis, RocksetDB

Expert’s cases:

1. Developed Deep Learning (DL) offload processor technology providing 10x-200x speedups with zero-code-change to customer’s Pytorch code, in AI training based on the type of DL workload. Working in a Mission Control Center position providing flight control to the International Space Station (ISS) Operations in the area of mission timeline development.

2. Built Global Caching System and Job Monitoring service with containerization using Docker to speed up the tensor-fetch time by 70-80% and reducing high egress charges for hosted data for cloud VMs.

3. Integrated Azure, AWS, GCP clouds and created job launch, monitor and stoppage automation for deep learning workloads.

4. Developed Scaletorch CLI interface with login, workload viewing, monitoring and stopping functionality using Python.

5. Lead a team of 5 engineers for comprehensive integration testing of the whole platform.