Manpreet Singh

Manpreet Singh

Data & Applied Scientist II

Microsoft

Manpreet Singh is a Data & Applied Scientist at Microsoft with close to seven years of experience advancing AI-driven solutions across cloud, enterprise analytics, and sales intelligence domains. He holds a B.Tech in Computer Science Engineering from SRM University and an MBA in Business Analytics (IB) from Symbiosis International University.

Prior to Microsoft, Manpreet held key data science roles at Oracle, VMware, and Cognizant, where he developed propensity-to-buy solutions, identity risk detection, and contract compliance—leveraging both classical ML and deep learning approaches.

He is the author and co-author of multiple peer-reviewed papers published in the Microsoft Journal of Applied Research (MSJAR) and RADIO, VMware’s internal R&D forum. His contributions extend to multiple patent filings with the USPTO. In addition, he is the creator of customdnn, a deep learning Python package designed to simplify the learning of neural networks, with over 80,000 downloads

RAHAT (Responsive AI Helper and Tasker) is a multi-agent AI system designed to assist railway and airport passengers by providing intelligent, real-time responses to various travel-related queries. From getting live train status, platform details, and ticket waitlist information to locating station facilities and calling for emergency assistance, RAHAT leverages LLM-based agents and tool integration to simulate smart, interactive terminals. With built-in memory, voice support (optional), and the potential for hardware integration (e.g., kiosks or mobile bots), RAHAT redefines the way public information is accessed and services are delivered.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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