Sanjiban Sengupta is a software engineer with eight years of experience building data, web, and AI tooling, currently based near CERN in Geneva. He has a strong research-to-production track record—contributing to high-profile open-source projects such as Apache Arrow and ROOT, co-creating Substrait Fiddle, and implementing Arrow–Velox and complex data type support for Velox’s Python interface. His work spans C++, Python, ML model parsers (Keras/PyTorch/ONNX), and high-scale observability pipelines, reflecting fluency across systems, compute kernels, and inference engines. He combines academic rigor from IIIT Bhubaneswar and research stints at CERN with practical product delivery at startups and observability teams, and he’s mentored GSoC contributors and CERN students. Less obvious: he blends low-level engine design with user-facing tooling—closing the gap between data-spec standards and developer ergonomics.
7 years of coding experience
3 years of employment as a software developer
Bachelor of Technology - BTech, Computer Engineering, 8.79/10, Bachelor of Technology - BTech, Computer Engineering, 8.79/10 at International Institute of Information Technology, Bhubaneswar
ACM Winter School, Algorithms on Big Data & Machine Learning, ACM Winter School, Algorithms on Big Data & Machine Learning at The Institute of Mathematical Sciences, Chennai
Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics
Role in this project:
Back-end Developer & Documentation Specialist
Contributions:210 reviews, 16 commits, 24 PRs in 9 months
Contributions summary:Sanjiban primarily contributed to the Apache Arrow project by enhancing and clarifying the project's documentation. Their work involved updating documentation for Python and C++ integrations with Pandas, addressing type conversions, and clarifying compute function behavior. The user also implemented a feature to control the field delimiter in CSV writing and added a method to convert a Table to a RecordBatchReader. Furthermore, they introduced support for filename-based partitioning and addressed issues related to NaN values in Parquet predicate push-down, demonstrating a focus on data processing and format interoperability.
The official repository for ROOT: analyzing, storing and visualizing big data, scientifically
Role in this project:
Back-end Developer & ML Engineer
Contributions:199 reviews, 37 commits, 33 PRs in 1 year 6 months
Contributions summary:Sanjiban's commits primarily revolve around enhancing and modifying the `RModel` class within the ROOT framework, particularly related to the SOFIE (Sophisticated and Optimized Framework for Inference and Evaluation) project. Their work involved implementing serialization, refactoring code to move functionalities related to SOFIE and ONNX parser to different directories. Furthermore, they added support for parsing and integrating models from Keras and PyTorch. The user also added a custom operator and implemented functionalities for handling layers like batch normalization, concatenate and reshape in Keras.
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.