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Lead ML Engineer | Ex-Microsoft | IIT Delhi
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Assessing your cultural and operational fit
Lead ML Engineer with 9+ years of experience in applied machine learning with companies like Microsoft (search engine) and Mobileum (fraud detection) and Pitney Bowes (location intelligence). Have trained and deployed multiple deep learning networks from scratch to production and have worked with different kinds of datasets including 2D/3D images, videos, text, and structured tabular data.
Simon Fraser University
Master of Science - MS, Computer Science
January 1, 2021 – January 1, 2022
Indian Institute of Technology, Delhi
Bachelor of Technology - BTech, Engineering Physics
January 1, 2012 – January 1, 2016
Theory+Practice
Lead ML Engineer
May 1, 2024 – Present
Vancouver, British Columbia, Canada
Theory+Practice
Senior Data Scientist
January 1, 2023 – May 1, 2024
Vancouver, British Columbia, Canada
Theory+Practice
Data Scientist
November 1, 2022 – January 1, 2023
Vancouver, British Columbia, Canada
Simon Fraser University
Graduate Research And Teaching Assistant
October 1, 2021 – December 1, 2022
Microsoft
Data Scientist 2
April 1, 2019 – September 1, 2021
Mobileum
Data Scientist
August 1, 2017 – April 1, 2019
Pitney Bowes
Machine Learning Associate
December 1, 2016 – August 1, 2017
Innoplexus
Data Engineer
June 1, 2016 – October 1, 2016
Building Science Models (Toys from Trash Project)
September 1, 2014 – January 1, 2016
Project inspired by IIT alumnus Arvind Gupta, where we formulated and designed 200+ models using trash to explain subtle science principles to underprivileged kids.
Machine Learning Data Lifecycle in Production
DeepLearning.AI
June 24, 2026 – Present
Fundamentals of Reinforcement Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse experience across different companies (Microsoft, Mobileum, Pitney Bowes, Theory+Practice) and academic background (IIT Delhi, Simon Fraser University) suggests adaptability. The 'Toys from Trash Project' indicates a social consciousness and initiative beyond typical technical roles. The progression within Theory+Practice shows commitment and growth. However, the project descriptions are brief, limiting a deeper understanding of collaborative or team-oriented contributions. The target role of 'NLP Engineer' is well-aligned with their ML background, especially the experience in enterprise search relevance.
Soft Skills & Operational Fit
The candidate's experience as a Lead ML Engineer and Data Scientist suggests leadership potential, problem-solving abilities, and a structured approach to ML projects. The 'Toys from Trash Project' indicates initiative and a commitment to explaining complex principles, which could translate to good communication and mentorship skills. However, without specific assessment data, a definitive evaluation of soft skills and operational fit is limited.