Machine Learning Engineer building AI-driven, production-ready systems
I'm Bhim Prasad Adhikari, an aspiring Machine Learning Engineer focused on computer vision, RAG applications, and full-stack AI products that ship on Google Cloud and Vercel.
Hiring snapshot
AI systems across computer vision, conversational search, and full-stack product delivery.
Primary stack
Python, TypeScript, FastAPI, Next.js
AI focus
CNNs, RAG systems, multi-agent workflows
Cloud delivery
Google Cloud Run, Vertex AI, Vercel, Docker
Current stage
B.Tech CSE (AI & ML), graduating in 2028
Featured live build
AI-powered agriculture platformCNN-based plant disease detection, Vertex AI insights, and contextual support for farmers.
Signal first: applied AI experience, cloud delivery, and recruiter-ready context
I build AI-driven systems that solve real problems, from plant disease detection and conversational retrieval to document understanding and full-stack product delivery. This page is structured for hiring managers who want to evaluate fit quickly.
Built an AI-powered agriculture platform with CNN-based plant disease detection and Vertex AI insights.
Designed RAG chatbots with LangChain, Pinecone, Express, Docker, and Google Cloud Run.
Built multi-agent AI workflows with Google Document AI, Gemini, and vector search.
B.Tech in Computer Science with AI & ML specialization at CV Raman Global University, graduating in 2028.
Machine Learning Engineer · Full-Stack AI Developer
Bhim Prasad Adhikari
CV Raman Global University · B.Tech CSE (AI & ML)
Featured Case Studies
The projects below are the best quick read for recruiters evaluating AI systems, product engineering, and cloud-ready delivery.
A multi-model AI life coach agent built with LangGraph that understands your voice, sees your images, and speaks back to you. Powered by open-source models Llama 3.3, Whisper and SD XL.
Insights & Tutorials
Thoughts on software development, AI, and continuous learning.
Classification of Flowers In Mobile Phone ( PART 2)
We build, train, and fix a deep neural network that learns to tell sunflowers from daisies. Then we ship it as a .tflite file, ready for your phone.
Building Generative AI from Scratch: A Complete Guide to Image Generation
Learn how to build image-generating AI models from the ground up. This hands-on tutorial walks you through the mathematics, architecture, and implementation of generative models including VAEs and GANs, with code you can run yourself.
Classification of Flowers In Mobile Phone ( PART 1)
In this first part, we'll dive deep into custom image preprocessing pipelines, dataset preparation with TensorFlow, and performance optimization techniques to get your data ready for model training.