I recently moved to Amazon in their fast growing Last Mile wing, enabling a seamless and smooth delivery of tens of Billions of packages each year. Here I work on systems relating to driver checkins, reservations, delivery sessions and return experiences carving out an easy and optimized journey for them. My team is behind 5+ Tier-1 services running at Amazon scale innovating for our customers and leading positive change in logistics ecosystems. Prior to this I was at Rippling, one of the fastest growing tech startups of the age. We were building a revolutionary tech platform to do away with all of the manual paper work from HR and IT, thus freeing smart people to work on hard problems. I particularly worked to scale payments and the core of payroll to serve our ever growing clientele and move billions of dollars in any week with utmost transparency and observability. Previously, I had a superb time working at the GS Equity Algo team in the low latency trading space and the high volume distributed graph based systems prior at the firm.
My bio from college is below in its unedited form, because much of it is still true (except that I graduated):
I am an undergraduate student at CSE, IITD. I am a tech enthusiast trying hands on scripting and computer automation, dev in C++/Java, Parallel programming paradigms and Data Science. Life is a continuous learning process, so work makes me more rigorous, travel makes my life more colorful. I enjoy trying out new things, food and ways of living life. I mostly code or travel.
My girlfriend recently inquired as to whether there will be an "About My Girlfriend" page / section. There will not. However, I’ll put a couple of salient facts here: in addition to being extremely beautiful, she is also an IIT trained engineer. She has driven a locomotive-train and promised to arrange a similar experience for me. She is big on fintech startups and usually high on life - apart from the time she is sleeping, which is more than one would think is possible. As a result of this exposure, I’ll hopefully also have some posts about startups and techniques to sleep during lectures.
Working on Transporter Eperience systems in the Last Mile Org at the world's largest logistic provider. Optimizing checkin, session and return workflows to enhance delivery partner experiences while also reducing cost of delivery. Scaling systems to enable seamless delivery of over 10s of Billions of packages each year.Sept. 2022
Working at core of Rippling's Payroll and Payments system, enabling movement of tens of billions of dollars in a week. Building robust ledger systems to enable transparent and observable flow of $$.Nov. 2021 - Sept. 2022
Built a large scale distributed layered graph to propagate health of systems (& programs) cutting accorss business lines thereby allowing root cause analysis and visualization of super-complex flows. Subsequently contributed to signals and trading strategies (algorithms) deployed by the firm in the US marketsJun. 2018 - Nov. 2021
Worked as a Data Engineer (Analyst) to develop a high volume - low latency data ingestion pipeline. Learnt to reason about semantics of distributed systems, their fault tolerance, resiliency and their consistency trade offs.May 2017 - July 2017
At Aalto, I learned about some state of the art ML techniques and developed a Graph Neural Network model to predict atomic charges in a molecule. (see git profile)Jun. 2016
In System Administration, I started to deal with servers, Linux systems, various network services and using scripts to manage massive departmental infrastructure.Sept. 2015 - May 2018
Worked on creating REST apis for data on video analytics. Also, indexing the largely unstructured MongoDB database for quick querying mechanism was another major part.Jun. 2015
Now I am at IIT Delhi trying out my hands on Data Science techniques and how parallel computing can be put to use in data mining and web intellignece.Apr. 2016
Graph Neural Network is a machine learning model that creates a graph of simple neural networks and analyses problems that can be framed as graphical model by their nature. It allows to apply the sophisticated neural network model to variable number of inputs and produce a single output based on graph or variabel number of outputs, one at each node.
Grievance Resolution System is a generic complaint system that is largely scalable because of use of RESTful apis. The repository links to an android client for the system. The complaint system can be used at large variety of organisations and is based on group based user management for better functionality