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AI Engineer with less than a year in Deep Learning & Multimodal AI
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Engineering undergraduate focused on deep learning, multimodal AI, and large language models. Experienced in re-implementing transformer architectures, building computer vision systems, and deploying end-to-end AI applications. Interested in AI research and engineering roles involving generative models, scalable training systems, and real-world deployment.
Pashchimanchal Campus, Institute of Engineering, Tribhuvan University
Bachelor in Computer Engineering · Computer Engineering
August 1, 2022 – June 30, 2026
SOS Hermann Gmeiner Secondary School
High School
June 1, 2018 – May 31, 2021
Nepal Applied Mathematics and Informatics Institute for research (NAAMII)
Research Intern
May 1, 2026 – Present
Kathmandu, Bagmati Zone, Nepal
Sambad Real-Time Communication Platform
June 27, 2026 – Present
Built a Discord-like chat application with server creation, authentication, and responsive UI. Implemented real-time messaging with WebSockets and audio/video communication using LiveKit. Used Next.js, MySQL, and Tailwind CSS for a production-style full-stack workflow.
View ProjectVision-Language Model (PaliGemma Reimplementation)
June 27, 2026 – Present
Reimplemented PaliGemma-style multimodal architecture using a SigLIP vision encoder and Gemma language model. Built the image embedding and conditional text generation pipeline in PyTorch. Tested inference using publicly available pretrained weights.
uTECHsil Real-Time Object Detection System
June 27, 2026 – June 1, 2024
Built a YOLOv8-based traditional object detection system during the YANTRA Hackathon. Created a custom dataset through web scraping and achieved mAP50 of around 0.9. Deployed real-time inference using Flask and React.
View ProjectLLaMA 3.1 8B Model Implementation
June 27, 2026 – Present
Reimplemented the LLaMA architecture from scratch, including tokenizer, ROPE, RMSNorm, SwiGLU, KV-cache, and grouped-query attention. Built a modular autoregressive inference pipeline and validated checkpoint loading using Hugging Face pretrained weights. Strengthened understanding of transformer internals, inference flow, and model architecture engineering.
View ProjectSwarlekha
June 27, 2026 – Present
Developed a multilingual zero-shot voice cloning and text-to-speech system for English and Nepali. Used Chatterbox / Resemble AI components with custom tokenizer and fine-tuning on a custom dataset. Built the inference pipeline for speaker-conditioned generation and speech synthesis.
View ProjectPlantCare Mobile Disease Detection System
December 1, 2024 – March 1, 2025
Collected and annotated a real-world cauliflower disease dataset from field conditions. Trained a YOLOv8-based detection model and deployed it in a React Native app with a Node.js backend. Added real-time detection, OTP verification, cloud integration, and voice-based interaction. Featured by Techpana.
View ProjectCauliflower Disease Detection using YOLO Models
IEEE International Conference on ICT and Photonics 2026
January 1, 2026 – Present
Deep Learning Specialization
DeepLearning.AI (Coursera)
July 1, 2024 – Present
Machine Learning Specialization
DeepLearning.AI (Coursera)
October 1, 2023 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from reimplementing advanced AI models to developing practical mobile applications and communication platforms, indicates a broad interest in technology and a strong drive for hands-on implementation. Their involvement in hackathons and AI schools, along with a research internship, suggests a collaborative and continuous learning mindset. The focus on generative AI, multimodal AI, and LLMs aligns well with an AI Engineer role, demonstrating a clear passion for the field. The academic background and high grades further support a strong work ethic and dedication.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and a proactive learning attitude through extensive personal projects and participation in hackathons and AI schools. Their ability to mentor at an AI bootcamp suggests good communication and leadership potential. The project descriptions indicate a structured approach to problem-solving and an understanding of full-stack development, which is beneficial for operational roles. However, without direct assessment data, specific soft skill strengths like teamwork or stress handling cannot be definitively quantified.