Architected and built high-volume systems using message brokers and Kubernetes with Kotlin on the AWS platform
Directly responsible individual (DRI) for cloud integrations with eSIM component vendors
Extended legacy serverless Node.js codebase to support new long-term business requirements
Established and debugged factory software integrations for new production lines for the Rivian R2. Walked factory line to ground system design in real-life context
Developed and maintained telecom software that powers enterprise call centers using Java, SQL and CCXML/VXML
Worked with other software developers, QA engineers and project managers on Scrum teams to iteratively add features and address defects in software
Architected, developed and deployed distributed monitoring and reporting solutions using RabbitMQ and Postgres
Served as technical lead/owner of mission critical software for high-availability systems
Met with project managers, engineers and stakeholders from other companies to understand their needs and develop custom systems to integrate with their proprietary infrastructure
Digested protocol specifications to create effective product integrations with existing protocols such as SMPP
Triaged and debugged live issues for production call centers handling 3,000+ simultaneous calls during critical service interruptions
Wrote documentation and automated tests using JUnit5 and Mockito to ensure maintainability
Served as a mentor and a technical resource for interns during their projects
Attended career fairs, reviewed technical assessments and interviewed interns for summer projects
Undergraduate Research Assistant (Sep 2019 - Dec 2020)
Responsibilities
Analyzed scientific problems to apply artificial intelligence and deep learning models
Studied and implemented existing machine, deep learning, and evolutionary programming models within larger systems to find better systems for approximating thermal properties of hypothetical materials
Rapidly learned new libraries and frameworks to implement them in more complex programs
Generated and analyzed charts and statistics to find flaws and improvements with models
Compared performance of statistical, neural network and genetic algorithm models against thermal conductivity data
Wrote and contributed to research articles within the fields of Computer Science/Materials Design
Preprocessed raw data and transformed it into a format interpretable by machine learning models
Cooperated with a team of researchers and experts to further the field of materials design
Kotlin 
Kubernetes 
DynamoDB 
Lambda 
Microservices 
Kafka 
Spring boot 
Datadog 
Node.js 
Java 
JUnit5/Mockito 
Maven 
Apache Tomcat 
Postgres 
Jenkins 
RabbitMQ 
REST APIs 
Websockets 
Linux 
SMS Systems 
Avaya telephony 
Python 
scikit-learn 
Numpy/Pandas 
Keras/Tensorflow 
Neural networks 
Genetic algorithms