• Created a scalable machine learning backend for OLX and enable faster build and deployment of machine learning models• Designed architecture and developed OLX Auto machine learning backend API which handles morethan 1 million requests daily, added 40+ models, deployed it on multiple regions and added support fordifferent machine learning model versions• Created a API's for moderators by which a moderator can search, moderate and edit ads, This tool was5 times faster than the previous tool and also reduced the AWS costs by 5X• Reduced time by 60% to build and release code by implementing parallel processing in different services and optimizing docker build pipelines for 2 env's, which impacted directly 15+ developers in the team.• Tech Used: Python | Django | celery | Machine Learning | Docker | Kubernetes | Redis

• Engaged in an onsite opportunity to untangle a complex business problem and helped a Singapore based client to build a data pipeline and data warehouse using apache airflow and spark• Developed Python scripts for data analytics team for GCP operations and integrated with R using reticulate, which helped all R/Python developer to upload and retrieve their studies from the blob storage• Created a Distributed API’s Gateway for executing the data science models usingpython/Django and celery and integrated all the DS models on it • Deployed centralized infrastructure monitoring system using grafana, influxdb, telegraf and delivered a tech talk, which helps in visualizing directly all the deployed projects on staging and production and setup alerts on it • Setup NIFI cluster for data pipelines and set up a benchmark for other projects to integrate with NIFI • Worked on shinyproxy and docker for deploying the R application to the web and converted 25+ standalone R application to the web applications for multiple countries • Deployed the Cloudera data science workbench for executing R application on a cluster • Migrated all the web applications to Docker. impacting directly 80+ developers/testers. • Created API's for data science Tech Used: python/Django, Redis, celery and uwsgi • Worked on Azure data bricks and apache airflow for pipelines and replaced all the corn jobs • Tech Used: Python, R, Docker, Redis, Airflow

• Created a data pipeline from the scratch for data processing and optimized the process by setting up the clusters• Part of Airtel's Network Digitization team, processing 15 +TB data daily. Solved complex business use cases with the goal of Improving the NETWORK EXPERIENCE of Bharti Airtel• Tech Used: apache Nifi, Superset, Druid, python, scala, apache airflow, influxdb, grafana, pyspark.

• Worked with a team that built & scaled the back-end product from scratch for a B2C/B2B product for hyper-local on-demand logistics delivery carriers. We scaled to 2000 deliveries a day with a company strength of 20 people. • Pivoting the product to a logistics aggregator, focusing on providing better experience & in-depth analytics to e-commerce websites, and filling in gaps left in traditional courier companies.• Created all internal tools, plugins, and assets for managing the day to day operations involving more than twenty thousand deliveries a day. • Conceptualized & built out the back-end for our very own e-commerce website from scratch.• Constantly evolving the product for better customer engagement, increasing order conversion rates, and decreasing RTO's• Development of APIs and backend, optimizing server-side code, load balancing, systems for automatic generation of daily reports and also developing shipment modules for e-commerce platforms like Magento, Shopify, etc.• Tech Used: Python, Django, Celery, Redis, AWS, RabbitMQ, Flower