About Me

Machine Learning Engineer | Full Stack Developer

isaurabhk27@gmail.com
(212)-518-3455
Saurabh Khandelwal

Hi there! Welcome to my website

I am passionate about problems with high impact and I love to code. I strongly believe that technology has a way to solve our problems to an extent one cannot imagine. I have worked with variety of completely novel machine learning problems and their unique dataset. I am always out for a discussion on Machine Learning or Software Development or technology in general.

I am currently working at Amazon in Artifical General Intelligence as Machine Learning Engineer team where we are pursuing payment method products in Brazil marketplace.

Past Experiences

Machine Learning Engineer at Behavidence
(2020 – 2021)
First machine learning engineer at Behavidence Inc.

  • Engineered features to compute mental health score based on mobile phone usage and built machine learning models in python that diagnosed depression with the recall rate of 75% and precision of 71%
  • Developed a real-time pipeline using Amazon Web Services Sagemaker to calculate mental health score – optimized a pipeline that ran for 24 hrs to 5 seconds
  • Built data pipeline using AWS services (Glue, Sagemaker, DynamoDB, API Gateway) fetching data from mobile application to train machine learning models

Software Developer at Atlassian Corporation PLC(2019- 2021)
Full stack developer at Atlassian Marketplace team.

  • Ideated an app recommendation system using association rule mining technique in Atlassian embedded marketplace. Developed the first backend prototype in Kotlin that constitutes 3.5% of app installs
  • Fixed license generation asynchronous flows across multiple Java services to reduce app installation failure by 35%
  • Optimized load management and batch emailing pipeline to launch request app feature on the embedded marketplace which led to 1.2M USD revenue in the following quarter
  • Updated MongoDB data model and exposed GraphQL API for the Cloud Fortified App program – to provide end customers a set of extensively secured top 10% of cloud apps
  • Redesigned UI filters on the marketplace homepage by developing reusable React components in Javascript which led to 4.3% increased filter usage
  • Created a Kotlin microservice and designed DynamoDB schema to build scalable distributed storage, and query systems that are fault-tolerant and easy to manage to facilitate migration of on-prem apps to the cloud with Rest APIs
  • Secured marketplace against privilege escalations and XSS attacks. Implemented the SLOs as API latency and availability of EC2 machines on SignalFX(terraform), providing real-time SLO violation alerts