Tarun Karuturi
Sr .NET Full Stack Developer at American Airlines
Maryville, Missouri, United States
Summary
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RockstarTarun Karuturi is a Sr. .NET Full Stack Developer with over a decade of experience delivering web-based, client-server, and N-tier solutions across finance, healthcare, telecom, and aviation. He specializes in backend systems with ASP.NET Core and C#, front-end interfaces using modern HTML5/CSS3/JavaScript frameworks, and cloud deployments on Azure and AWS, with practical expertise in microservices and CI/CD. At American Airlines and prior employers he has driven API-first architectures, database optimization, and Agile delivery while mentoring teams through the full SDLC. Unusually for a primarily Microsoft-focused engineer, he also contributes to high-profile open-source ML infrastructure (pytorch) work around codegen, serialization and performance tracing, bringing an MLOps-aware perspective to production engineering. Known for pragmatic problem-solving and strong communication, he combines hands-on coding with system-level thinking to ship reliable, observable systems.
4 years of coding experience
8 years of employment as a software developer
Github Skills (17)
pytorch10
serializable10
serializer10
python10
machine-learning10
serialization10
deserialization10
performance-optimization10
c-language9
codegen9
deep-learning9
cprogramming-language9
devops8
neural-network7
autograd7
Programming languages (2)
Github contributions (5)
pytorch/pytorch
Jul 2022 - Jan 2025
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
MLOps Engineer
MLOps EngineerContributions:27 reviews, 21 PRs, 22 pushes in 2 years 6 months
Contributions summary:Tarun primarily contributes to the ExecuTorch framework, focusing on improving the codegen layer, serialization, and integration of the event tracer for performance analysis. They implemented support for edge dialect operations in serialization and deserialization processes. Their work includes enabling operator profiling and adding logging for Evalue outputs in the codegen, enhancing debugging and optimization capabilities. Additionally, they have optimized partitioning by caching DFS paths and utilized export_for_training.
pythongpu-accelerationdeep-learninggpunumpy
tarun292/pytorch
Mar 2023 - Sep 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:39 pushes, 7 branches in 6 months
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