AI/ML Engineer
I build intelligent systems — from data-driven models to scalable ML pipelines. One years delivering AI solutions that learn, adapt, and perform at scale.
# AI/ML Engineer: Pukar Rimal from brain import creativity, logic, data import numpy as np import tensorflow as tf stack = ["Python", "ML", "DL", "NLP", "LLMs"] def ship(idea): return f"{idea} → deployed!" print(ship("AI systems that adapt"))
I'm an AI/ML engineer based in Kathmandu, passionate about building intelligent systems that learn, adapt, and solve real-world problems. I specialize in crafting models, pipelines, and solutions that scale seamlessly.
My philosophy: AI is future, lets be part of it. I develop models that are interpretable, pipelines that are robust, and systems that deliver impact efficiently. I integrate the build model in the system reducing hallucinations and delivering desired output
When I'm not training models or exploring datasets, I'm experimenting with new AI frameworks, reading about neural networks, or exploring ways to blend AI with creative projects.
Fine-tuned Unsloth LLaMA-3.1 on domain-specific datasets to enhance reasoning and context understanding. Achieved improve response accuracy, faster inference, and robust performance on specialized NLP tasks.
Developed a Retrieval-Augmented Generation (RAG) legal assistant app, leveraging Qdrant as the vector database for fast, context-aware document search. Enables users to query legal texts and receive accurate, AI-generated guidance instantly. It is based on Nepal Constitution.
Built a Retrieval-Augmented Generation (RAG) app that ingests PDFs as input and provides accurate, context-aware answers. Leveraged ChromaDB for efficient vector storage and fast retrieval, enabling users to query documents seamlessly.
Developed an intelligent job portal that analyzes user CVs and recommends relevant jobs using AI algorithms. Features real-time search, skill-based matching, and personalized career guidance powered by ML models.
Built a CNN-based image classifier that distinguishes between nature and city images. Trained on diverse datasets, achieving high accuracy and robust performance for real-world image classification tasks.
Built a system that automatically analyzes news content to detect misinformation and flag potentially fake articles. Designed for accurate, fast, and reliable classification of news items.
Developed AI-driven recommendation systems to deliver personalized content suggestions. Built and deployed OCR applications for automated text extraction from images and documents. Designed live web crawling pipelines for dynamic quiz generation, handling pagination and workflow automation. Integrated LLM-powered systems with advanced prompt engineering, enhancing response quality and reducing hallucinations. Built a Retrieval-Augmented Generation (RAG) system to combine LLMs with external knowledge sources for more accurate and context-aware outputs. Developed RESTful APIs for AI-driven workflows using FastAPI to streamline backend operations. Containerized AI systems using Docker and deployed them on cloud infrastructure for scalable hosting and accessibility.
Researched and evaluated data crawling and scraping strategies for dynamic and large-scale websites. Designed and maintained scalable web crawling pipelines using Crawl4AI for high-volume data extraction. Cleaned and normalized unstructured web data using LLM-based workflows to improve downstream usability. Developed crawling configuration files to support pagination, session handling, and workflow automation. Performed unit testing using pytest also conducted end-to-end system testing. Performed prompt engineering to improve LLM response quality and reduce hallucination
Open to AI/ML & software engineering roles and interesting side projects.