Turning some idea into a web app or just watching netflix
Backend and applied-ML engineer with experience building and optimizing high-traffic services. I work where dependable infrastructure, quantitative systems, and practical machine learning meet—from low-latency C++ execution paths to full-lifecycle MLOps pipelines for algorithmic models.
Here is my Github for more info on my projects,
Twitter casual convo?, Linkedin for everything else.
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Engineered collaboratively with various Departments at the University of Maryland, Built a robust and automated langchain pipeline to process research data via an ensemble of Amazon Nova Premier and Nova Lite models to ensure federal compliance of research proposals.
Stack: Streamlit, Python, MySQL, Langchain, AWS Bedrock, Hugging Face, AWS
Spearheaded the development of a dynamic Grain budgeting and data assessment tool, leveraging a comprehensive database to provide farmers with real-time insights into their crop budgets and financial planning.
Stack: NextJS, Radix UI, AWS Amplify, DynamoDB, AWS
Worked on the and
platforms,
to leverage zoominfo's best in class contact and buying signal data to provide customers with an end-to-end pipeline that can, source and hire a candidate or convert buyer research into actionable sales prospects!
Stack: AngularJS, NestJS, Groovy on Grails, Apache Solr, Jenkins, Google Cloud
Bringing real estate to the 21st century,
our vision is to change how real estate tranasctions are managed by title companies and real estate vendors.
Stack: NextJS, Express.js, MySQL, Github Actions, Vercel