Tino Thayil
Data Engineer II
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolTino Thayil is a Data Engineer II based in Seattle with nine years of experience building data pipelines and applied ML tooling across startups and enterprises including multiple tenures at Amazon and CVS Health. He combines a strong academic foundation (MS in Computer Science from Stony Brook) with hands-on engineering—from co-founding a London AI startup to production data roles at large organizations. Tino contributes to open-source ML evaluation tooling (notably work on the ragas library for LLM application evaluations), showing a focus on reliable metrics and embeddings integration. He excels at refactoring evaluation code and adapting testset generation to real-world workflows, bridging research-quality methods and production constraints. Colleagues would describe him as an innovative problem-solver and dreamer who deliberately explores emerging tech to deliver practical impact. Based in Seattle, he brings entrepreneurial curiosity plus proven delivery in data engineering and ML infrastructure.
9 years of coding experience
4 years of employment as a software developer
High School Computer Science, High School Computer Science at St. Antony's Public School, KanjirapallyBachelor of Technology - BTech Computer Science, Bachelor of Technology - BTech Computer Science at APJ Abdul Kalam Technological UniversityMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Stony Brook University
English, Malayalam, Hindi
Github Skills (7)
mlops10
python10
evaluation10
llm10
datasets9
sentence-transformers9
huggingface8
Programming languages (4)
Github contributions (5)
explodinggradients/ragas
Oct 2023 - Feb 2024
Supercharge Your LLM Application Evaluations 🚀
Role in this project:
ML Engineer
ML EngineerContributions:5 reviews, 21 PRs, 29 comments in 3 months
Contributions summary:Tino primarily contributed to the `ragas` library, focused on evaluating LLM applications. Their work involved modifying and refactoring code related to evaluation metrics, specifically the context_relevancy and context_precision. They also made changes to the testset generator to ensure compatibility with the evaluation process and added a class for FastEmbed embeddings.
evaluationllmllmops
tinomaxthayil/ragas
Nov 2023 - Mar 2024
Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
Contributions:1 PR, 46 pushes, 17 branches in 4 months
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