Abhijeet Mazumdar
Founder And CTO at Industrolearn
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolAbhijeet Mazumdar is an AI engineering leader and fractional CTO who helps founders and enterprises rapidly adopt agentic AI workflows, build MVPs, and upskill teams across regulated and startup environments. With a decade of experience bridging research and production—from building NexGPT at Intel to architecting FedRAMP-aligned SaaS for DoD customers—he combines systems-level ML engineering with product leadership. He’s founded multiple ventures and non-profits (Industrolearn, ArthaNex, Industrodrome) that turn academic research into deployable products and talent pipelines. Technically fluent in RAG, LLM fine-tuning, high-throughput inference, and AI IDEs like Cursor and Claude Code, he’s also an active OSS contributor to Transformer Lab and Kubernetes tooling. Notably, he has driven measurable business impact (multi-million ARR, inference optimizations to 450 tokens/sec) while coaching cross-functional teams to adopt AI-first development practices. Why is Melody so Chocolatey?
4 years of coding experience
2 years of employment as a software developer
Master's degree, Cloud Architect, Master's degree, Cloud Architect at SimplilearnBachelor’s Degree, Electronics and Instrumentation Engineering, Bachelor’s Degree, Electronics and Instrumentation Engineering at SRM UniversityIndian Institute of Technology RoorkeePost Graduate Program, AI and Machine Learning, Post Graduate Program, AI and Machine Learning at Purdue UniversityMaster’s Thesis Research, Optimal Control Systems, Master’s Thesis Research, Optimal Control Systems at Technische Universität Berlin
English, Hindi, Bengali, Odia, Telugu, German
Github Skills (8)
lora10
recommender10
rlhf9
electron9
transformers9
llama8
kubernetes6
system6
Programming languages (4)
Github contributions (5)
abhimazu/abhimazu
Mar 2022 - Feb 2025
Contributions:6 pushes, 1 branch in 3 years
abhi-mazu/time_series_framework
Feb 2022 - Mar 2023
This framework automates the modeling of critical signals using multiple training algorithms to find the most accurate prediction model by finding the best correlation with other available signals. Additionally, it inferences live signal data and predicts critical signals using the best model generated previously. It compares the predicted and the actual signal value to raise warnings if anomaly is detected.
Contributions:2 PRs, 3 pushes, 2 branches in 1 year
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