Dipam Chakraborty
Machine Learning Engineer at H2O.ai
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolDipam Chakraborty is a Machine Learning Engineer based in Bengaluru with a decade of experience and five years focused on deep learning, computer vision, model deployment, and hardware acceleration. He has applied these skills across industry and research—working on automotive defect detection, machine learning competitions, and published academic work—while currently contributing at H2O.ai after roles at AIcrowd and Intel. Dipam combines strong Python engineering and pipeline optimization with hands-on RL integration experience, notably adding PettingZoo and RLlib support to the CityLearn environment to enable multi-agent demand-response research. His background in robotics and embedded systems (developing control, vision, and electronics for an AUV) gives him uncommon practical fluency in taking models from prototype to resource-constrained hardware. He stays close to current research trends and emphasizes reproducible, production-ready ML systems that balance performance with deployment constraints.
10 years of coding experience
7 years of employment as a software developer
CourseraUdacityB.Tech + M.Tech Dual Degree, Electronics and Communication Engineering, B.Tech + M.Tech Dual Degree, Electronics and Communication Engineering at National Institute of Technology RourkelaHigh School, High School at Adamas International School
English, Hindi, Bengali
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
machine-learning10
dev-environment10
python10
rllib10
reinforcement-learning10
development-environment10
data-visualisation9
data-visualization9
data-visualizations9
computer-vision4
Programming languages (2)
Github contributions (5)
intelligent-environments-lab/CityLearn
Jun 2022 - Jul 2022
Official reinforcement learning environment for demand response and load shaping
Role in this project:
ML Engineer
ML EngineerContributions:12 commits, 10 pushes, 1 comment in 1 month
Contributions summary:Dipam primarily contributed to integrating the CityLearn environment with reinforcement learning frameworks. This involved developing a PettingZoo wrapper and examples for RLlib, enabling multi-agent reinforcement learning within the environment. They also worked on fixing rewards and done conditions within the PettingZoo wrapper. Further contributions included adding rendering capabilities with random values and plot generation.
reinforcement-learningdeep-reinforcement-learningdemandshapingdemand-response
dipamc/InertialNav
Jun 2017 - Dec 2018
Contributions:8 pushes, 1 branch in 1 year 5 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.