Reach out: arkadipmaitra at gmail dot com
Working on distributed AI systems at Red Hat, contributing upstream to the PyTorch ecosystem. The work spans multi-node communication, training correctness, on-device inference, and research in computer vision, sign language, and mechanistic interpretability of reasoning in language models.
Distributed training and communication form the core focus — fixing correctness and reliability issues in collective operations, transport layers, and multi-backend coordination for large-scale GPU clusters. This includes work on failure-path determinism, lifecycle management, and making communication behave predictably across hardware topologies.
On-device and edge inference work involves extending operator coverage, fixing kernel correctness, and improving build-system plumbing for selective compilation on resource-constrained targets.
Inference diagnostics and tooling contributions center on building source-level tracing and adapter infrastructure that enables any framework built on PyTorch to expose its internal call paths for debugging and profiling.
Developer tooling and framework integration work includes compiler interactions with distributed subsystems, autograd correctness for tensor subclasses, and improving error messages to surface real problems faster.
Served as a reviewer for IEEE Transactions on AI, the official PyTorch blog, and the PyTorch Associate certification course. Program committee member for MCP Developer Conf Bangalore and CFP reviewer for MCP Dev Summit across Mumbai, Bangalore, and Seoul.
Gave talks on distributed communication, PyTorch internals, and collective operations at BangPypers, Razorpay Engineering Meetup, HuggingFace Meetup Bangalore, and multiple university sessions. Super mentor at the HuggingFace-Meta Hackathon.
Published author on the Red Hat Developer blog, writing on distributed communication infrastructure in PyTorch.