Shreeharsh Shinde

About Shreeharsh Shinde

Computer Science undergraduate focused on backend systems, distributed architecture, and scalable product design.I work primarily with Go, Python, and TypeScript, with strong foundations in Java (OOP, multithreading) and C++(DSA & competitive programming). My interests lie in system design, concurrency, data consistency, and cloud-native infrastructure.I actively contribute to open source — including MapLibre GL JS and other cloud-native ecosystems — and have worked on Kubeflow-related projects, deepening my understanding of ML pipelines, compiler internals, and distributed systems.I’m particularly interested in the trade-offs between monoliths and microservices, autoscaling systems (Kubernetes), API design, and reliability engineering. I enjoy studying real-world production architectures and translating theory (e.g, DDIA concepts) into practical backend decisions.Outside engineering, I play chess to sharpen strategic thinking and study orchestral music for structure and depth — both influencing how I approach systems: patiently, analytically, and with long-term vision.Always building. Always refining.

Experience

1. Open Source Contributor

   Kubeflow

   Dec 2025 — Present

   Contributed to Kubeflow Pipelines (CNCF), focusing on SDK correctness, compiler behavior, and deployment reliability.Added typing.Literal support to the SDK compiler with validation and IR-level tests for safer pipeline definitions.Improved dsl.ParallelFor retry semantics, ensuring retries are applied per item rather than at the aggregate level.Fixed Kustomize manifest warnings by removing unused variables, improving install-time signal quality.Refactored SDK executor imports to reduce side effects and improve runtime behavior.Worked closely with maintainers through reviews, rebases, and CI fixes across Python and Go.

Education

• Walchand College of Engineering(A Govt. Aided Autonomous Institute),SANGLI-M.S

  Bachelor of Technology - BTech, Computer Science

  2022