Working with the Cloud Infrastructure team responsible for deploying IBM Watsonx.data, a high-performance Data Lakehouse onto AWS and IBM Clouds.

Worked with the Cloud Infrastructure team responsible for deploying IBM Netezza, a high-performance Database server onto Azure and IBM Clouds.

Setup an Enterprise SonarQube server for Static Code Scanning of Netezza code, and automated scans using a CI pipeline. Developed a CD pipeline to deploy the Netezza Image onto multiple IBM Cloud and Azure clusters using OpenShift.

Deployed a Python Flask server on an auto-scaling multi-node cluster using Kubernetes with IBM Kubernetes Service.

Successfully presented and handed-over this pipeline to the security team to run scans on-demand via IBM Cloud.

Setup OnePipeline cronjobs to perform role based access synchronization between Azure Security Groups, Access Management tool and role based access controls in OpenShift clusters, which are running every hour.

Developed a CD pipeline to deploy Netezza onto multiple IBM Cloud and Azure clusters using OpenShift.

Actively Participated in Architecture design discussions with Senior Engineers for setting up these pipelines on IBM Cloud.

Developed a Service for visualizing the network topology of device connections inside a customer’s network which reduces the time taken by an analyst to draw the topology of a network from a couple of hours to microseconds.

With an Ember.js Redux front-end, Spring Boot back-end, I got to develop a Fullstack application from scratch. Integrated 5000+ lines of code into the production codebase after testing it thoroughly using Unit and Integration tests.

Built CI/CD pipelines using Jenkins and Python for automating the testing of Threat-content rules in our SIEM product. This eliminated the need for software testers to spend time manually testing these rules which used to take a couple of days daily. Now with an automated CI/CD pipeline that task is done in 6 hours.

Practiced Agile development methodologies using SCRUM in a collaborative environment in Git.

Cherished having complete ownership of my project - being able to demo my project to all product managers, VPs and end-users, incorporate feedback, propose new stretch goals, and work on additional features.

Worked as the head Teaching Assistant for CS 4120 Natural Language Processing under Professor Lu Wang for Spring 2020 semester.

Duties include creating/optimizing assignment and exam material, managing grading infrastructure, mentoring final projects, and giving lectures/presentations to a class of 50 students.

Mainly responsible for brainstorming on various product features, implementing them, maintaining the codebase, training and analyzing results of Machine Learning models and deploying them to production using AWS.

Maintained and expanded our Cloud Architecture using EC2 servers on AWS Cloud for Machine Learning APIs. Built an auto-scaling serverless REST API for keyword extraction using AWS Lambda, API Gateway, S3, and boto3.

Developed 4 scalable Natural Language Processing RESTful APIs by hosting them on AWS using Flask, uWSGI, and NGINX webservers, which helped generate a secondary monthly income for the company.

Improved the accuracy of an existing 17 Class News Classifier from 81% to 97% using a One-vs-Rest Strategy on an SVM Classifier which allowed us to deploy the feature live on the product without any/little human interference.

Integrated a Queuing Mechanism in our ML Backend using RabbitMQ, which prevented our Deep Learning APIs to break due to memory shortage when too many requests are flooded into our ML Servers.

Restructured the entire ML Architecture by shifting to Docker Containers, adding Unit tests and Style Checks to all projects and setting up Jenkins for continuous integration in production which facilitated faster, automated deployments, saving time and efforts of the entire team.