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Machine Learning, AI
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Carnegie Mellon University
Master’s Degree, Computer Systems Networking and Telecommunications
January 1, 2015 – January 1, 2017
Amrita Vishwa Vidyapeetham
Bachelor of Technology (BTech), Electrical, Electronics and Communications Engineering
January 1, 2009 – January 1, 2013
Stealth Startup
Co-Founder
August 1, 2025 – Present
San Francisco Bay Area · Hybrid
Pear VC
Member + FFC Fellow (ML/AI)
March 1, 2025 – Present
San Francisco Bay Area · Hybrid
Gypsum AI
Co-Founder and CTO
August 1, 2024 – July 1, 2025
San Francisco Bay Area · Hybrid
Lyft
Staff Machine Learning Engineer/ Manager
April 1, 2023 – July 1, 2024
Lyft
Senior Machine Learning Engineer
July 1, 2021 – April 1, 2023
Lyft
Machine Learning Engineer
March 1, 2020 – July 1, 2021
Criteo
Software Engineer - Machine Learning
January 1, 2019 – January 1, 2020
San Francisco Bay Area
Veritas Technologies LLC
Software Engineer - Machine Learning
January 1, 2017 – January 1, 2019
San Francisco Bay Area
Veritas Technologies LLC
Product Innovation Intern
January 1, 2016 – January 1, 2016
California
Carnegie Mellon University
Graduate Teaching Assistant
January 1, 2016 – January 1, 2017
Pittsburgh
Cisco Systems
Network Engineer II
January 1, 2013 – January 1, 2015
Bangalore
Classroom Activities and On/Off Task Behavior in Elementary School Children[R]
February 1, 2017 – March 1, 2017
Generated business insights on given data set by first exploring the data using descriptive statistical methods. Cleaned the dataset, used feature engineering method such as one-hot encoding, applied stepwise methods and information gain for feature selection and tested PCA for feature space reduction. Constructed the problem as a classification problem and tested it on various models like logistic regression, KNN, Naïve Bayes, SVM and random forest using k-fold cross validation to select best model based on metrics such as accuracy, AUC score and ROC curve. Also, used decision trees on different subsets of data sets to gain more interesting business insights.
Information Retrieval and Search Engines[Java]
September 1, 2016 – December 1, 2016
Designed and implemented a Text based Search Engine using CLueWeb09 dataset as test case. The data was indexed using open source (Apache Lucene) indexing. Implemented various exact-match (Ranked and Unranked Boolean) and best-match (Indri, Okapi BM25) retrieval models and query operators (NEAR, WINDOW, WAND and WSUM) Query reformulation, query expansion and Learning to Rank capabilities were also added to the search engine.
Movie Data Analyser [Python]
September 1, 2016 – November 1, 2016
Gained experience with the full data science pipeline through this project. Started by first mining the dataset through IMDBpy API, then applied known methods of data cleaning and feature engineering on collected dataset. Visualized the data using matplotlib, word cloud to gain interesting insights into data. Created revenue predictor using logistic regression and movie rating classifier using random forests.
Machine Learning [Python]
January 1, 2016 – February 1, 2016
Implemented a neural network with one hidden layer and Hidden Markov Models based on given training and development datasets, optimizing performance, used Back propagation algorithm. Implement a Naive Bayes classifier to classify a political blog as being “liberal" or “conservative”, trained the classifier from set of self-identified liberal and conservative blogs, analyzing the effect of Smoothing.
Social Network: a Micro-blog Designed and Implemented in Python & Django
January 1, 2016 – March 1, 2016
It is a featureful, interactive web application includes user registration and authentication, email integration for user verification, photo upload, and quasi-real-time updates.
Advanced Cloud Computing
January 1, 2016 – February 1, 2016
Built a private cloud using OpenStack platform and added capabilities of a simple Load Balancer and Auto Scaling Group. Using OpenStack APIs for telemetry, the Auto Scaling Group retrieves the CPU Usage of each instance and monitors the average reported load on those machines. This data was used to enabled scale-in/scale-out operations on cloud. Parallelized a serial, iterative, big data processing application (model training component of a recommendation system), adapted it to execute in the Spark framework (a generalization of Map Reduce), and tune it for cost-performance running on AWS
Gravel: A RTOS Kernel for ARM processor
December 1, 2015 – Present
Designed and implemented a shared memory, pre-emptive, multitasking RTOS kernel for the ARM processor that includes: - System calls - Interrupt handlers for systems calls and IRQs - Timer driver - Rate monotonic task scheduling - Context switching - Support for mutex
Multithreaded Web Proxy with Caching
December 1, 2015 – Present
Developed a concurrent web proxy that accepts HTTP GET requests and also implemented a system that caches objects obtained from the HTTP web server.
Tiny Shell
December 1, 2015 – Present
Implemented a simple Linux shell capable of forking and handling multiple child processes. The shell supports both background and foreground processes, I/O redirection and built in commands.
Dynamic Memory Allocator
November 1, 2015 – Present
Designed and implemented a general purpose dynamic memory allocator which provides functionalities similar to malloc(), calloc(), realloc() and free() using block coalescing to improve memory utilization. Optimized for high memory utilization and throughput using segregated free list for managing the free blocks.
Cache Simulator
October 1, 2015 – Present
Designed and Implemented a cache simulator to demonstrate cache miss/hit/evict based on the memory addresses accessed with LRU replacement policy. Later optimized matrix transposition to make best use of cache.
Facial/Voice Recognition based Biometric Security System
February 1, 2013 – Present
Developed a biometric system that comprises of facial detection and voice recognition using MATLAB. Used the Viola Jones algorithm and implemented Facial Recognition using Eigen Faces and Voice Recognition using MFCC in order to authenticate users.
CCNA Routing and Switching
Cisco
June 24, 2026 – Present
NI-Labview
Cranes Software International Limited
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
CCNA Voice
Cisco
June 24, 2026 – Present
TI TMS320 C5515
Ashfaq Ibrahim, Sr. Vice President
June 24, 2026 – Present
CCNP Voice
Cisco
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's entrepreneurial background and experience in fast-paced tech companies like Lyft and Criteo suggest a proactive, innovative, and results-oriented mindset. Their diverse project portfolio, including personal projects in various domains, indicates intellectual curiosity and a drive for continuous learning. The transition from Network Engineer to ML Engineer demonstrates adaptability and a strong desire to pivot into cutting-edge fields, aligning well with a dynamic AI-focused environment.
Soft Skills & Operational Fit
The candidate's experience as a Co-Founder and CTO, along with their role as a Staff Machine Learning Engineer/Manager at Lyft, suggests strong leadership, project management, and strategic thinking abilities. Their involvement in organizing events as a Lead Intern and TA role indicates good communication and mentoring skills. The diversity of projects, from low-level systems to high-level AI applications, demonstrates adaptability and a broad operational fit.