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GenAI, Advertising, e-Commerce
I am an Applied Scientist with over seven years of hands-on experience in architecting large-scale distributed model training platforms. My expertise encompasses the development of innovative deep learning algorithms for search and recommendation engines, as well as the enhancement of ad relevance and quality. In my current role, I harness multi-modal datasets—including text, images, tables, and graphs—and utilize cutting-edge Transformer-based models to address intricate challenges in the realms of e-commerce and advertising. My fervor for artificial intelligence is deeply anchored in my academic pursuits and research interests. I am a proud alumnus of Rutgers University, where I earned a Ph.D. in Computer Science and an M.S. in Electrical and Computer Engineering. I am committed to pushing the envelope in machine learning and computer vision, translating research breakthroughs into tangible solutions that enrich the experiences of countless customers and businesses. Expertise: * Domains: Deep Learning, Natural Language Processing, Information Retrieval, Ads * Languages: Python, SQL, Java, C++ * Platforms & Tools: Unix, Linux, AWS, Apache Spark, PyTorch, Microsoft DeepSpeed * Academic Credentials: Ph.D. in Computer Science and M.S. in Electrical and Computer Engineering from Rutgers University; B.Tech. in Electrical Engineering from the College of Engineering, Pune, India.
Rutgers University
Doctor of Philosophy (Ph.D.), Computer Science
January 1, 2010 – January 1, 2016
Rutgers University
MS, ECE
January 1, 2009 – January 1, 2011
COEP Technological University
B. Tech, Electrical
September 1, 2004 – May 1, 2008
Microsoft
Applied Scientist
May 1, 2025 – Present
Greater Seattle Area · Hybrid
PayPal
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Amazon
Applied Scientist Intern (Search and Discovery Technologies)
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Greater Seattle Area · On-site
ExxonMobil Corporate Strategic Research
Research Intern
May 1, 2014 – August 1, 2014
Clinton, Annandale, New Jersey · On-site
Rutgers University
Instructor
June 1, 2013 – August 1, 2013
Rutgers University, Piscataway, New Jersey.
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for an ML Engineer role, having worked in similar capacities at major tech companies known for innovation and fast-paced environments. The diversity of projects, from advertising optimization to catalog quality and inventory placement, shows adaptability and a broad application of ML skills. The academic background and research experience further indicate a drive for continuous learning and problem-solving, aligning well with a research-oriented engineering culture.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight collaboration with multidisciplinary teams, translating business requirements into technical solutions, and mentoring TAs, suggesting strong communication and leadership potential. The role as an instructor also points to an ability to explain complex concepts clearly. However, without specific psychometric or communication test results, a definitive assessment of soft skills and operational fit is limited.