Jan 2019 – Sep 2022 · 3 yrs 8 mos · San Francisco Bay Area

Aug 2016 – Jan 2019 · 2 yrs 5 mos · San Francisco Bay Area

• Lead a team of local and remote engineers to accomplish several deliverables with aggressive deadlines
• ⠀⠀⠀ * Reduce operational cost by 70% by migrating geocoding for all our apps from Bing Maps to Mapbox.
• ⠀⠀⠀ * Pilot packet sniffing using Scapy to calculate NetFlow from multiple home IoT devices and aggregate it to detect vulnerability.
• ⠀⠀⠀ * Incorporate sign-up and auth mechanism for Verizon Smart Family app by integrating with carrier platforms.
• ⠀⠀⠀ * Architect an in-house push notification solution and migrated our mobile clients from PubNub for better reliability and debug capabilities.
• ⠀⠀⠀ * Defined and improved data collection pipelines by using AWS Kinesis and Redshift.
• Build fully automated CI/CD pipelines for containerized applications using Jenkins, Spinnaker, Kubernetes, Terraform, and AWS.
• Evaluate feasibility of Datadog in a company Hackathon, which has now become an integral part of our monitoring stack.
• Collaborate with product, design, and project teams to define high-level milestones and transform them into structured engineering tasks.
• Plan low-downtime cross-team feature releases and work with the Operations team to deploy and debug on production servers.
• Revamp our onboarding tutorials to use our latest tech stack, enabling new hires to contribute within the first week.
• Mentor engineers by guiding them through code reviews to achieve the best development, testing, logging, and monitoring practices.

Jul 2014 – Aug 2016 · 2 yrs 1 mo · San Francisco Bay Area

• Transitioned our entire legacy stack to Ansible to reduce deployment times by 50% for all our parental controls products.
• Added backend support for processing Apple In-App purchase for our Family Locator iOS apps.
• Worked on a team that led API-first development effort and redesigned our backend monolith into microservice and event-driven architecture.
• Implemented several horizontally scalable microservices in Python Flask and Java Spring Boot deployed in Docker containers.
• Ensured product success and uptime by debugging customer issues and providing on-call production engineering support.

Jul 2013 – Jul 2014 · 1 yr · San Francisco Bay Area

Nov 2011 – Jul 2013 · 1 yr 8 mos · San Diego, California, United States

• Automated periodic fetching, reporting, and parsing of logs generated at remote access points to provide a statistical insight on the overall system behavior.
• Developed a man-in-the-middle CLI tool to listen, filter, or drop messages between system components.
• Rectified and enhanced our messaging libraries to support end-to-end payload generation and verification.
• Accelerated the backend regression test cycles by 80% by creating a simulator for third party applications.
• Designed a REST client interface and XML data parser to facilitate debugging of our deliverable API.
• Built tools to generate real-time statistics and visualizations to monitor our pilot system of 150 devices.
• Implemented a client for Qpid AMQP broker to send user data derived through a load test model.

Oct 2010 – Dec 2012 · 2 yrs 2 mos · San Diego, California, United States

• mHealth Project
• Part of a multidisciplinary group working on research, modeling, and analysis of biometric sensor data.
• Developed a GUI for running data mining and signal processing algorithms and querying for patterns of well-being from heart-rate variability time-series. Designed an algorithm for finding synchronicity and causal relations among individuals in group activities like Yoga.
• Formulated upper bound on the samples required to implement the method in real time with <5% error.
• Analyzed the accelerometer data to search specific events for classifying human gait and checking for gait symmetry.
• Made the tools portable to run on a backend engine for user physiological data analytics and visualization.
• Continued to volunteer the effort even after graduating. The work resulted in 1 journal, and 2 conference publications.
• Technologies used: Python, Matlab

Sep 2010 – Dec 2010 · 3 mos · San Diego, California, United States

• TA to Prof. Robert Lugannani for the upper division course "Probability and Random Processes for Engineers"

Jul 2010 – Sep 2010 · 2 mos · San Diego, California, United States

• Cognitive Networking
• Implemented a machine learning technique to forecast the best configuration for a Wireless LAN system.
• Worked with a team to develop and test the probabilistic graphical model, Bayesian Networks, on an embedded device for real-time traffic prediction.
• Improved error performance, precision and throughput by 35%, 40% and 20% respectively.
• Extended goals to publish a paper for an international conference as well as a demo event and presented the project at university research exposition.
• Technologies used: Matlab, C, MySQL, Shell scripting, R

Mar 2010 – Jun 2010 · 3 mos · San Diego, California, United States

• Devised a decentralized strategy for network nodes to choose a Wi-Fi channel adaptively using a Markov Random Field and Maximum A Posteriori framework.
• Programmed the model for channel classification in Python using historical data to attain 95% accuracy.