Aman Dalmia is an AI engineer with a decade of experience building practical AI products that expand access to technology for underserved communities. He designs and ships end-to-end systems—from lightweight mobile inference and data pipelines to no-code evaluation studios—currently leading voice-agent simulation at Artpark and building AI copilots for nurses and educators at Noora Health and HyperVerge Academy. His open-source work includes Plio, which turned YouTube videos into interactive lessons used by tens of thousands of learners, and contributions to scikit-learn documentation and reinforcement-learning implementations. Aman combines research-grade ML (KDD and ICLR–linked projects at Wadhwani AI) with product execution and developer tooling, making complex models usable in low-resource settings. He often partners with nonprofits and runs workshops and playbooks to help teams evaluate and integrate AI responsibly. Based in Bengaluru, he mixes hands-on engineering with a clear focus on social impact and scalable, reproducible tooling.
10 years of coding experience
5 years of employment as a software developer
Bachelor of Technology (B.Tech.) Electronics and Communication, Bachelor of Technology (B.Tech.) Electronics and Communication at Indian Institute of Technology, Guwahati
Computer Science, Computer Science at Coursera
Self-Driving Car Nanodegree Artificial Intelligence, Self-Driving Car Nanodegree Artificial Intelligence at Udacity
High School Science, High School Science at Bhavan's Gangabux Kanoria Vidyamandir
Climate Change: Learning for Action Environmental Education, Climate Change: Learning for Action Environmental Education at Terra.do
Notes for the Reinforcement Learning course by David Silver along with implementation of various algorithms.
Role in this project:
Back-end Developer
Contributions:84 commits, 2 PRs, 73 pushes in 1 month
Contributions summary:Aman primarily contributed to the implementation of algorithms related to Reinforcement Learning, specifically policy iteration and value iteration. They added skeleton code files and completed the policy iteration, including fixing indentation errors and adding debug statements. The user's work focused on developing the core functionality related to dynamic programming algorithms within the reinforcement learning context.
Contributions:17 commits, 44 PRs, 454 comments in 11 months
Contributions summary:Aman primarily focused on improving the documentation within the scikit-learn repository. Their contributions involved adding and correcting documentation, including adding :user: role to whats_new, explaining the physical meaning of ellipsoids, adding a warning regarding the relationship between C and alpha, and adding information about CircleCI build artifacts. They also addressed documentation-related bugs and fixed typos, ensuring the clarity and accuracy of the documentation.
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