Sandeep Manganellore
Technology Architect at Cognizant
Charlotte, North Carolina, United States
Summary
🤩
Rockstar🎓
Top SchoolSandeep Manganellore is a Technology Architect with over a decade of hands-on Java/J2EE experience designing and delivering enterprise web and integration solutions across banking, airlines, and healthcare. He combines deep backend expertise—Spring, Spring Boot/Batch, JPA, web services, JMS and PL/SQL—with practical skills in performance tuning, CI/version control, and Unix scripting to drive reliable production systems. At Cognizant he architects scalable applications and previously led development across TCS and smaller firms, contributing across the full SDLC from requirements and UI design to deployment. An active open-source contributor, he has improved documentation for Apache MXNet and implemented MLOps integration and tests for AWS multi-model serving, reflecting an interest in making ML tooling production-ready. Detail-oriented yet strategic, he pairs tactical code-level problem solving with long-term architectural thinking.
12 years of coding experience
11 years of employment as a software developer
Bachelor of Engineering (B.E.), Instrumentation Technology/Technician, Bachelor of Engineering (B.E.), Instrumentation Technology/Technician at Dr Mgr Engineering college
English
Github Skills (15)
api-documentation10
mxnet10
deeplearning-ai10
deep-learning10
inference10
python10
ai10
server10
documentation10
dockers9
docker9
testing8
cicd7
github7
onnx6
Programming languages (6)
Github contributions (5)
apache/mxnet
Oct 2016 - Aug 2021
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:
Technical Writer
Technical WriterContributions:1 release, 10 reviews, 79 commits in 4 years 11 months
Contributions summary:Sandeep's commits primarily focused on improving the documentation of the `apache/mxnet` repository. They made numerous edits to documentation files, including correcting spelling and grammar, reorganizing the documentation structure, and fixing broken links. They also added a tutorial template and example code, demonstrating a clear emphasis on enhancing the clarity, organization, and usability of the project's documentation for users.
pythonschedulerdataflowmutationdata-science
awslabs/multi-model-server
Oct 2017 - May 2018
Multi Model Server is a tool for serving neural net models for inference
Role in this project:MLOps Engineer
MLOps EngineerContributions:7 commits, 1 PR, 1 branch in 6 months
Contributions summary:Sandeep contributed to the integration and testing of a deep learning model server, specifically for an SSD (Single Shot MultiBox Detector) model. They added example code and integration tests for the SSD model, incorporating steps to export, deploy, and test the model. The user also refactored the integration tests module and updated references to the relevant deployment infrastructure. This involved modifications to the service configuration and the service code itself.
pytorchmxnetservingdeep-learninginference
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