Sankalp Sanand
AI Developer at Xanadu
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolSankalp Sanand is an AI developer with a decade of software engineering experience, currently working at Xanadu in Toronto and focused on advancing next-generation AI systems. He combines hands-on backend and DevOps expertise—demonstrated through significant contributions to Agnostiq's Covalent project for orchestrating ML, HPC, and quantum workflows—with practical experience at DataRobot and Agnostiq. Sankalp has a strong academic foundation from an MS in Computer Science at Illinois Institute of Technology and a BS from SRM IST Chennai. He’s comfortable refactoring complex dispatch logic and improving CI/CD pipelines to make heterogeneous compute environments more reliable and maintainable. Known for a clear obsession with AGI, he pairs research-oriented ambition with pragmatic engineering that ships production-ready tooling.
10 years of coding experience
4 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at Illinois Institute of TechnologyBachelor's degree Computer Science and Engineering, Bachelor's degree Computer Science and Engineering at SRM IST Chennai
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
workflow-engine10
workflow-automation10
orchestra10
orchestration10
devops10
python10
cicd10
pipeline10
parallelization9
packaging9
Programming languages (5)
Github contributions (5)
AgnostiqHQ/covalent
Jan 2022 - Jan 2023
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:344 reviews, 168 commits, 178 PRs in 11 months
Contributions summary:Sankalp primarily contributed to improving the Covalent project's functionality and maintainability. Their work involved fixing UI server installation issues, updating manifest and setup files, and refactoring code for improved dispatching. The user also made significant changes to the CI/CD pipeline by excluding unnecessary UI webapp files, disabling Slack notifications and version bumping. They also contributed to switching dispatcher logic from lattice.dispatch to ct.dispatch.
high-performancepythonworkflow-automationwatsonscience
kessler-frost/keypoint_detection
Apr 2018 - Jun 2021
In this project, I have combined my knowledge of computer vision techniques and deep learning to build an end-to-end facial keypoint recognition system. Facial keypoints include points around the eyes, nose, and mouth on any face and are used in many applications, from facial tracking to emotion recognition.
Contributions:1 PR, 4 pushes, 1 branch in 3 years 3 months
emotion-recognitionface-recognitionpointsnosekeypoints
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