Hemant Kashniyal is a seasoned software engineer with a decade of experience spanning driver and firmware development, high-performance IO for NVMe storage, and real-time sensor data processing for autonomous vehicle mapping. He has led AI/ML platform initiatives and distributed systems work, including back-end design for Jio AI’s Vision Platform and contributing MLOps infrastructure to Seldon Core, and he currently focuses on stream-first observability platforms at Chiron while advising a stealth startup. He excels at delivering scalable, maintainable systems and has a track record of client-focused project delivery in consulting settings. An active open-source contributor, he leverages Go, C++, Python and GRPC/WebRTC to build distributed pipelines, including RAG-based chat apps and high-capacity media delivery solutions. Based in Hyderabad, India, he holds an MTech in Information Systems from Netaji Subhas Institute of Technology and a BTech in Information Technology from Graphic Era. He thrives on learning new horizons and solving complex problems at the intersection of systems, AI/ML, and real-time data.
11 years of coding experience
14 years of employment as a software developer
MTech, Information Systems, MTech, Information Systems at Netaji Subhas Institute of Technology
Kendriya Vidyalaya, Pithoragarh
BTech, Information Technology, BTech, Information Technology at Graphic Era Deemed to be University
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Role in this project:
MLOps Engineer
Contributions:2 reviews, 17 commits, 3 PRs in 8 months
Contributions summary:Hemant's commits primarily focus on updating and modifying protobuf definitions and related dependencies within the `seldonio/seldon-core` repository. The changes suggest a focus on adapting protobuf specifications, potentially for model serving or data exchange within the MLOps framework. The commits also include adjustments to include protobuf files, which is fundamental to the project's core function of serving and managing machine learning models. These updates are indicative of a role focused on building and maintaining the MLOps infrastructure.
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