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Vice President, Goldman Sachs | Applied AI Platforms
We’re building autonomous search and data mining platforms that power multiple compliance-driven initiatives at Goldman Sachs. Previously, I helped drive AI capabilities within FactSet’s Machine Learning group, developing data models for real-time financial news and architecting large-scale, company-wide web test automation infrastructure. I started my journey at AWS, engineering components for S3’s durability pipeline to help uphold its strict “eleven 9s” data guarantee. I am passionate about architecting solutions to complex big data problems using ML, LLMs, and Agentic Systems. My interests lie at the intersection of Distributed Systems, Image Processing, Information Retrieval, and Data Mining. If you’re navigating similar technical challenges or building out next-generation data systems, feel free to connect.
University at Buffalo
Master of Science - MS (AI), Computer Science
January 1, 2014 – January 1, 2016
Guru Nanak Dev University, Amritsar
Bachelor of Technology - BTech, Computer Science and Engineering
January 1, 2010 – January 1, 2014
Goldman Sachs
Vice President
December 1, 2022 – Present
Goldman Sachs
Senior Software Engineer
May 1, 2019 – December 1, 2022
FactSet
Machine Learning Engineer
March 1, 2016 – May 1, 2019
Manhattan, New York, United States
Amazon Web Services (AWS)
Software Development Engineer Intern
May 1, 2015 – August 1, 2015
Seattle, Washington, United States
CDAC
Student Internship
June 1, 2012 – August 1, 2012
Chandigarh, India
Distributed Systems: Amazon Dynamo Style Replicated Key-Value Storage
March 1, 2015 – Present
• Implemented a Dynamo styled key-value storage with simultaneous availability and linearizability guarantees. • The app had the ability to successfully undergo read and write operations occurring with tolerance to node failures. • Performed chain replication in order to provide linearizability guarantee for operations occurring at the same time. • Conducted ring based routing with replication in order to provide availability guarantee.
Distributed Systems: Group Messenger with Total and FIFO Ordering Guarantees and Persistent Storage
March 1, 2015 – Present
• Designed and Implemented an algorithm that provides a Total and FIFO ordering guarantees while effectively handling device failures for a text messaging application. • Assigned sequence numbers to each of the messages with basic multicast implementation for ordering (ordering implies the receiving order based on the Lamport logical clocks). • Tested on a simulated real time messaging scenario by implementing the decentralized algorithm on a bunch of continuously failing Android devices communicating with each other.
Distributed Systems: Peer to Peer Distributed-Hash-Table based on Chord Protocol
March 1, 2015 – Present
• Implemented a Chord based DHT functionality on Android devices via messaging application (built as a part of a previous project). • Performed ID space partitioning/re-partitioning among the nodes with minimal relocation of partitions in case of join/leave. • Applied ring based routing for structured distribution. • Used SHA-1 hash function to lexically arrange nodes in a ring and serve insert/query operations with uniform load distribution.
Machine Learning: Classification of Handwritten Digits using Multi-Layer Perceptron Neural Network
March 1, 2015 – Present
• Classified MNIST dataset of handwritten digits consisting 60,000 training samples through Multilayer Perceptron (MLP) feed forward and back propagation algorithm. Received accuracy of ~96% on test data. • Used back propagation algorithm to learn the weights of neural network along with incorporation of regularization factor to prevent the over-fitting of data. • Tuned hyper-parameters of the Neural Network to study the impact on efficiency and accuracy of the network.
Distributed Systems: Messenger App on Android
February 1, 2015 – Present
• Developed a Group Messenger application on android using Socket IO programming. Implemented multicast and used AsyncTask API for multithreading.
Machine Learning: Diabetes Prediction
February 1, 2015 – Present
• Predicted diabetes level in patients using machine learning techniques viz. Linear Regression, Ridge Regression & Non-Linear Regression and compared their performance. • Performed classification on a sample data set using linear and quadratic discriminant analysis.
Image Processing: Image Depth Estimation to Generate Interpolated View
November 1, 2014 – Present
• Implemented block based matching and dynamic programming to estimate depth of the objects in view, • Applied consistency checks to evaluate the generated depth maps. • Synthesized interpolated view from two slightly shifted views if the objects in consideration.
Image Processing: Semantic Labeling of Images using SVM
November 1, 2014 – Present
• Classified oversegmented data set images into super pixels using SVM classifier and assigned semantic labels to each super pixel. • Extracted features from each labelled super pixel and used them to classify it among 8 different classes. • Graphically represented accuracy obtained for each class using different set of training images.
Image Processing: Photometric Stereo Implementation to Render 3D Shapes
September 1, 2014 – Present
• Used 2D images under different light sources to develop a consistent model. • Estimated albedo and surface normals at each point of the images to generate height map.
Information Retrieval: News Indexer
September 1, 2014 – Present
• Indexed Reuters news corpus and implemented Query Parser, BM25 (Okapi) relevancy and Vector Space Model on dataset of 10,000 plus news articles. • Implemented document parser, tokenizer, token filters, indexer and index searcher • Retrieved the results based on the Boolean Query using Interpreter design pattern and ordered results on the basis of Vector Space Model.
Information Retrieval: News Search Engine with Ad Serving
September 1, 2014 – Present
• Indexed NY Times news corpus and implemented Query Parser. • Indexed Bid data and Advertisement data and calculated the suggested bid for the advertiser to stay at the top. • Used Java Server Faces (JSF) along with jQuery used to build interactive user interface. • Built the system to serve contextual advertisements relevant to query as well as search results.
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Sequence Models
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Design and Analysis of Algorithms
Stanford University
June 24, 2026 – Present
Analysis of Algorithms
Princeton University
June 24, 2026 – Present
Introduction to Systematic Program Design
The University of British Columbia
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project history shows a strong inclination towards research and development in AI/ML and distributed systems, which aligns well with an innovative and data-driven culture. The progression through roles at Goldman Sachs (Senior Software Engineer to Vice President) suggests a commitment to growth and taking on increasing responsibilities. However, the projects are heavily skewed towards academic/personal research, and while impressive, there's less explicit mention of collaborative team projects or direct business impact outside of their professional experience descriptions. The target role is 'Data Analyst', but the experience and projects lean more towards 'Machine Learning Engineer' or 'Applied Scientist', which might indicate a slight mismatch in the explicit role title versus the candidate's demonstrated capabilities and interests. This could be a strength if the Data Analyst role involves significant ML/AI components, but a potential mismatch if it's purely descriptive analytics.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and the ability to work on complex, multi-faceted technical challenges. Experience in leadership roles at Goldman Sachs suggests potential for mentoring and driving initiatives. The diverse project portfolio demonstrates adaptability and a proactive approach to learning and applying new technologies.