Chaitanya Bapat
Senior Software Engineer at Meta
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top School
Top expert inComprehensive Tech Knowledge: Machine Learning, DevOps, and Software Engineering
Chaitanya Bapat is a Senior Software Engineer based in London with 10 years of experience building production-grade ML and distributed systems at Meta and AWS. He specializes in customer-facing account access and support ML at Meta and previously helped launch SageMaker Distributed Data Parallel and optimized MXNet operator performance as an Apache MXNet committer. Comfortable across C++, Python, Java and cloud-native tooling, he has a track record of reducing CI costs, improving training scalability, and shipping features that materially impact revenue and recoveries for high-value users. An active open-source contributor and mentor, he pairs deep technical rigor with a decade-long commitment to teaching students and early-career engineers how to enter big tech and pursue graduate study. He also maintains a creative side through regular technical writing and retained research roots from Georgia Tech, where he worked on decentralized credentialing early in his graduate studies. Unusually, his career blends low-level ML operator work (including CUDA/cuRAND-based operators) with measurable customer experience wins in product-facing systems.
10 years of coding experience
6 years of employment as a software developer
Secondary School Certificate, General Studies, 95.82%, Secondary School Certificate, General Studies, 95.82% at Parle Tilak Vidyalaya English Medium SchoolBachelor of Engineering (BE), Computer Engineering, 8.41, Bachelor of Engineering (BE), Computer Engineering, 8.41 at University of MumbaiHigh School Certificate, Science, 85.67%, High School Certificate, Science, 85.67% at Sathaye CollegeFirst Year Junior College, Commerce, 83%, First Year Junior College, Commerce, 83% at Narsee Monjee College of Commerce and EconomicsMasters of Science, Computer Science, 3.63, Masters of Science, Computer Science, 3.63 at Georgia Institute of Technology
English, Sanskrit, Spanish, Hindi, Marathi
Stackoverflow
3,751reputation
337kreached
45answers
55questions
pytorch
top-5%
Github Skills (26)
pytorch10
distributed-training10
python10
mxnet10
machine-learning10
horovod10
sagemaker10
aws10
test-automation10
testing9
dockers9
numpy9
docker9
cprogramming-language8
c-language8
Programming languages (15)
Github contributions (5)
apache/mxnet
Sep 2018 - Oct 2020
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
ML Engineer & Test Automation Engineer
ML Engineer & Test Automation EngineerContributions:30 reviews, 102 commits, 161 PRs in 2 years 1 month
Contributions summary:Chaitanya contributed to the development and testing of the MXNet deep learning framework, primarily focusing on adding functionality and ensuring the quality of the library. They implemented a new `randint` operator and added unit tests to verify its correctness. Additionally, the user fixed shape mismatches, debugged operators for `isfinite`, `isinf` and `isnan`, and added new operators. Moreover, the user was involved in fixing flaky tests and ensuring the accuracy of various operators by adding more test cases.
pythonschedulerdataflowmutationdata-science
aws/deep-learning-containers
Jun 2020 - Feb 2021
AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.
Role in this project:ML Engineer
ML EngineerContributions:76 reviews, 12 commits, 15 PRs in 8 months
Contributions summary:Chaitanya contributed to the development and testing of machine learning models within the AWS Deep Learning Containers repository. Their work involved modifying Dockerfiles to include dependencies and tests for Horovod, a distributed training framework. They also added telemetry support for monitoring and debugging MXNet-based models. Furthermore, they worked on testing and integrating PyTorch 1.7.1 with SageMaker's distributed data-parallel capabilities.
pytorchsagemakercontainersmxnetserving
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