Ananth Sankaralingam
Co-Founder at chromie.dev
New York City Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolAnanth Sankaralingam is a Co-Founder and computer science student at the University of Maryland with a decade of hands-on experience building backend systems, ML pipelines, and research-grade model compression. He has shipped production-focused tooling at Amazon and contributed MLOps features to Kubeflow Pipelines—adding Sagemaker training support and integration tests for a widely used ML orchestration project. His internships span LinkedIn, Children’s National Hospital (graph RAG for surgeons), and Experian, where he identified $400K in cloud savings, showcasing a blend of research, product, and cost-conscious engineering. Currently balancing startup leadership at chromie.dev with LLM compression research accepted to top conferences, he pairs practical backend expertise with deep learning and VR interests. Notably, he’s built internal RAG chatbots and secure backend pipelines, reflecting a knack for turning cutting-edge research into usable systems.
10 years of coding experience
1 year of employment as a software developer
High School Diploma, High School Diploma at South Brunswick High SchoolBachelor's degree Mathematics and Computer Science, Bachelor's degree Mathematics and Computer Science at University of Maryland
Github Skills (12)
kubeflow10
machine-learning10
mlops10
aws10
python10
sagemaker10
dockers9
kubernetes-pods9
docker9
kubernetes9
testing8
cicd7
Programming languages (5)
Github contributions (5)
kubeflow/pipelines
Sep 2022 - Dec 2022
Machine Learning Pipelines for Kubeflow
Role in this project:
MLOps Engineer
MLOps EngineerContributions:40 reviews, 7 commits, 17 PRs in 3 months
Contributions summary:Ananth primarily contributed to the integration testing and open sourcing of the v2 AWS TrainingJob component within the Kubeflow Pipelines repository. They focused on enhancing the component with new Sagemaker training features, including parameters and updated integration tests. The user refactored code, addressed documentation issues, and updated the sample MNIST pipeline to utilize the new hosting components, showcasing their expertise in building and deploying machine learning pipelines. Furthermore, the user adapted existing integration tests and made changes to the paths of models.
pipelinetektondata-sciencemachine-learningmlops
ananth102/ack-runtime
Jun 2021 - Apr 2023
Common ACK runtime and type system
Contributions:2 releases, 7 pushes, 2 tags in 1 year 9 months
ackruntimetype-system
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