Aniket Maurya
Chief Executive Officer at ODF
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAniket Maurya is a founder and engineering leader with nine years of experience building production-grade AI systems and developer tools, now leading Celesto AI as CEO to make long-running agent deployment as seamless as Vercel made web apps. He previously built LitServe at Lightning AI—doubling throughput for real-time model serving—and contributed to prominent open-source projects like PyTorch Lightning and Lightning-Flash, improving CI, documentation, and model/backbone integrations. His hands-on background spans ML systems, RAG/LLM multi-agent pipelines, and scaling generative models to thousands of concurrent users, demonstrating both product and infrastructure chops. A London-based founder and ODF Fellow, he blends deep technical execution with startup leadership—and quietly ships infrastructure improvements (like dead-code CI checks) that keep complex ML projects reliable.
9 years of coding experience
7 years of employment as a software developer
CBSE XII, CBSE XII at St. Anne's High School, Patna CityBachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at Kalinga Institute of Industrial Technology, Bhubaneswar
Hindi, English
Stackoverflow
365reputation
65kreached
23answers
0questions
Github Skills (26)
pytorch10
artificial-intelligence10
python10
machine-learning10
cicd10
image-segmentation10
deeplearning-ai10
deep-learning10
segmentation10
ai10
computer-vision10
data-science9
testing9
documentation8
gradio7
Programming languages (13)
Github contributions (5)
Lightning-Universe/lightning-flash
Feb 2021 - Aug 2022
Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains
Role in this project:
ML Engineer
ML EngineerContributions:21 reviews, 17 commits, 19 PRs in 1 year 6 months
Contributions summary:Aniket's contributions primarily revolve around integrating and extending the capabilities of the `lightning-flash` library. They added support for timm backbones, expanding the model options available for vision tasks. They also integrated with segmentation_models.pytorch, enhancing semantic segmentation functionalities. Furthermore, the user documented the use of timm backbones and addressed other issues related to documentation and dependency management.
torch-geometricicevisionconfigurepytorch-lightningdomains
Lightning-AI/pytorch-lightning
Jun 2021 - Nov 2022
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
Role in this project:MLOps Engineer
MLOps EngineerContributions:24 reviews, 5 commits, 16 PRs in 1 year 5 months
Contributions summary:Aniket contributed to improving the project's build and testing infrastructure by integrating a dead code checker (Vulture) into the CI workflows. They also updated and maintained the codebase, including removing deprecated functionalities like LightningModule.summarize and LayerSummary/ModelSummary. Furthermore, they were involved in the integration of a full example within the documentation of LightningLite and added title and description features to ServeGradio functionality.
pythonheadachespytorch-modelsdata-sciencehandling
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