👋 Hey, I’m Pramod
I just wrapped up my Master’s in Data Science from Tribhuvan University. Before that, I did my Bachelor’s in CSIT, and for the past 5+ years I’ve been working as a software engineer, building cool stuff for companies in Nepal and the US. Along the way, I’ve contributed to projects that made a real impact, and even spent some time as a lecturer at Himalayan College of Management, sharing what I know.
I’m a lifelong learner always curious, always exploring. These days, my focus is on Data Science and Machine Learning, and I’m excited to dive deeper into this world and find opportunities where I can grow while solving interesting problems.
My favorite languages for systems programming, software engineering, and data analysis.
My preferred technologies for Machine Learning and Data Science Projects.
My preferred technologies for back-end web programming and database architecture.
My favorite tools for version control, code editing, and container orchestration.
While I was engaged in seva, I had the opportunity to collaborate with US-based firms where we harnessed various technologies, including AWS, ElasticSearch, Laravel, Python, PHP, Django, and MySQL. My responsibilities was incrafting high-performance APIs, integrating third-party APIs and developing a multi-tenant system. In addition to these endeavors, I also contributed to internal projects at seva, such as the CV management system.
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Uptrendly is an influencer marketing firm headquartered in Nepal. The platform registers social media influencers, allowing brands and businesses to leverage these influencers for promoting their products and services. Within the platform, brands have the ability to initiate campaigns, choose influencers, monitor campaign progress, and access detailed campaign reports. I played a pivotal role in developing the platform's backend infrastructure
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Devanagari digit recognition system have mainly two components. Webapp in flask and recognition model. Model is trained on handwritten devanagari digits. Model have shown, 97% accuracy in the testing data.
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