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Passionate and driven AI Engineer with 2 year of hands-on experience in building and deploying cutting-edge Generative AI solutions.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
py-youtube-search
January 26, 2026 – Present
A lightweight, asynchronous Python library to search YouTube videos programmatically without an API key. It scrapes search results using aiohttp and re, making it fast, robust, and perfect for high-performance applications.
View ProjectData-Prep-for-LLM-fine-tuning
May 13, 2025 – November 20, 2025
This repository helps prepare datasets for fine-tuning Large Language Models (LLMs). It includes tools for cleaning, formatting, and augmenting data to improve model performance. Designed for researchers and developers, it simplifies the data preparation process for efficient training.
View ProjectFine-Tuning-Siglip2-Vit-Model
April 8, 2025 – April 9, 2025
This repository offers tools and guidance for fine-tuning the Siglip2 Vision Transformer (ViT) model. It includes scripts and best practices to adapt the model for custom datasets and tasks. Designed for researchers and developers, it ensures efficient fine-tuning and optimal performance for vision-based applicatio
View ProjectStructured-Output-Examples-for-LLMs
March 20, 2025 – August 16, 2025
This repository demonstrates structured data extraction using various language models and frameworks. It includes examples of generating JSON outputs for name and age extraction from text prompts. The project leverages models like Qwen and frameworks such as LangChain, vLLM, and Outlines for Transformers models.
View ProjectDocker-OpenCV-CUDA-Builder
March 8, 2025 – December 10, 2025
This project provides a Docker-based environment for running OpenCV with CUDA support, enabling GPU-accelerated computer vision tasks. The setup includes a complete build of OpenCV 4.12.0 with CUDA 12.8.1 and cuDNN support.
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and demonstrate a strong interest in cutting-edge AI/ML topics, particularly LLMs and computer vision. This aligns with a culture of innovation and continuous learning. However, the lack of team-based projects or professional experience makes it difficult to fully assess collaboration and broader cultural fit. The project diversity is good within the AI/ML domain, but there's no information on non-technical contributions or community involvement.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.