Shivam Soni
Software Engineer at Prama AI
Tempe, Arizona, United States
Summary
🤩
Rockstar🎓
Top SchoolShivam Soni is a pragmatic software developer with 6 years of experience building cloud-native, high-performance backend systems and full‑stack applications across finance, healthcare, and enterprise domains. He excels in Java, Spring Boot, distributed microservices, AWS, and integrating AI/ML models into production—recently deploying BERT/RoBERTa-based recommendations that improved advisory accuracy by 24%. Shivam has a strong track record of reducing latency and improving reliability (27% API latency reduction, 99.9% uptime contributions) through performance tuning, observability, and CI/CD-driven Kubernetes deployments. He pairs backend expertise (Kafka, gRPC, Hibernate) with frontend skills (React, TypeScript, GraphQL) to deliver end-to-end features and dynamic dashboards that boost decision-making speed. Known for clean architecture and test automation, he routinely leads cross-functional design, enforces coding standards, and uncovers non-obvious performance bottlenecks. Based in Tempe and grounded in practical engineering, he’s seeking backend or full‑stack roles where scalable systems and measurable business impact matter.
7 years of coding experience
2 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Arizona State University
Github Skills (29)
representation-learning10
pytorch10
pre-trained-model10
semantic-segmentation10
imaging10
ieee10
keras10
unet-image-segmentation10
image-segmentation10
unsupervised-learning10
segmentation10
self-supervised-learning10
resnet10
medical-image-analysis10
foundation-models10
Programming languages (3)
Github contributions (4)
MrGiovanni/UNetPlusPlus
Nov 2020 - Feb 2021
[IEEE TMI Best Paper Award] Official Implementation for UNet++
Contributions:11 commits, 11 pushes in 3 months
deep-learningimage-segmentationunet-image-segmentationimagingtensorflow
MrGiovanni/ModelsGenesis
Sep 2020 - Feb 2021
[MICCAI 2019 Young Scientist Award] [MedIA Best Paper Award] Models Genesis: self-supervised pre-training for 3D medical images. Learns transferable representations from unlabeled CT and MRI volumes, then fine-tunes for downstream segmentation and classification. Keras and PyTorch weights included.
Contributions:6 commits, 6 pushes in 4 months
3dclassificationkeraspytorchsegmentation
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