Sai Enduri
Senior Software Engineer at Mastercard
Dublin, Ireland
Summary
🤩
Rockstar🎓
Top SchoolSai Enduri is a Senior Software Engineer based in Dublin with a strong background in cloud and DevOps, blending seven years of hands-on infrastructure work with recent senior roles at Mastercard. He has deep AWS, Terraform, Kubernetes/EKS, Docker, Jenkins and Ansible experience, having built CI/CD pipelines, automated recoveries and reduced cloud costs through resource optimization. Sai pairs practical ops proficiency with software skills—authoring Python automation, Groovy shared libraries and infrastructure-as-code—to deliver highly available, secure production environments. He also contributes to ML compiler work upstream, adding tensor.reshape lowering and broader ElementsAttr support to the IREE project, demonstrating a bridge between systems engineering and model compilation. A MSc in Data Analytics complements his operational focus, enabling data-driven improvements and reporting automation across platforms. Known for on-call ownership and pragmatic automation, he reliably turns complex operational requirements into repeatable, resilient systems.
2 years of coding experience
6 years of employment as a software developer
Bachelor of Technology - BTech Computer Science, Bachelor of Technology - BTech Computer Science at Teegala Krishna Reddy Engineering CollegeMaster of Science - MS Data Analytics, Master of Science - MS Data Analytics at Dublin Business School
Github Skills (11)
compiler-optimization10
compiler10
machine-learning10
compiler-compiler10
mlr10
tensorflow9
cprogramming-language9
c-language9
vulkan8
cuda8
python7
Programming languages (9)
Github contributions (5)
iree-org/iree
Oct 2023 - Mar 2025
A retargetable MLIR-based machine learning compiler and runtime toolkit.
Role in this project:
ML Engineer
ML EngineerContributions:129 reviews, 87 PRs, 102 pushes in 1 year 5 months
Contributions summary:Sai contributed to the IREE compiler project by implementing patterns to lower tensor.reshape operations, specifically focusing on converting them into flow.tensor.reshape ops. They also worked on generalizing code to support ElementsAttr, moving away from a reliance on DenseElementsAttr. Furthermore, the user added IREE compiler implementations for dense resource usage and enhanced the test suite. They are involved in model compilation and optimizations.
mlirspirvvulkantensorflowcompiler
saienduri/iree
Apr 2024 - Mar 2025
A retargetable MLIR-based machine learning compiler and runtime toolkit.
Contributions:190 pushes, 45 branches in 10 months
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.