I am Akhil, an AI & Data Science Engineer building intelligent systems with focus, clarity, and deep learning. Bridging the gap between raw data and profound insights.
I am Akhil, a Computer Science student, currently based in Lucknow. I am an AI and Machine Learning Engineer who loves building smart systems that can think and learn. My main focus is on Large Language Models (LLMs) and creating the 'brain' (backend) that makes apps work smoothly.
In my work, I don't just write code, I look for the story behind the data. I use tools like Python, Pandas, Scikit-learn, and Machine Learning to turn raw information into useful insights. I am also active in Open Source.
I believe in discipline, whether I am at my desk or in the gym. To me, training a computer model is a lot like personal fitness: it takes patience, a strong foundation, and a focus on getting better every single day. I am driven to build technology that is not only powerful but truly helpful for the real world.
Babu Banarasi Das University
Sep 2022 – Jun 2026
Lucknow, UP
Pursuing a comprehensive curriculum focused on core computer science logic, algorithms, and advanced computing. Cultivating a specialized interest in Artificial Intelligence and system architecture while actively contributing to open-source initiatives.
Sun Flower Public School
Apr 2021 – Mar 2022
Ballia, UP
My Intermediate years were focused on building a solid foundation in Advanced Mathematics. I spent a lot of time practicing Integrals, Derivatives, and Matrices. It was a journey of constant growth, where every new chapter challenged me to think differently.
Sun Flower Public School
Apr 2019 – JMar 2020
Ballia, UP
High school was the first time I faced serious academic goals. Preparing for my Board exams required a lot of hard work, consistent practice, and focus. It was a journey of building confidence, making mistakes, and realizing that effort is the most important part of any achievement.
Developed a machine learning model that predicts high blood pressure risk with 85% accuracy. By analyzing a dataset of over 5,000 patient records, the system identifies critical risk factors using a Random Forest classifier.
A Telegram bot delivering instant and scheduled news. Built using Python, LangChain, Groq, LLaMA-3.3, and the Tavily API via an autonomous Agentic AI architecture.
I built an AI tool that reads a sentence and automatically decides if the meaning is Positive or Negative. By training the model on thousands of movie and product reviews, the system can understand human emotions in text with over 90% accuracy.
Ai-Code-Assistant is our collaborative B.Tech project developed by our Team. It integrates the MERN stack with the Gemini API to provide real-time AI code auditing and peer-to-peer video communication.
A hands-free, voice-activated personal desktop assistant built with Python named Nexus AI Assistant. This assistant leverages the power of Google's Gemini 2.5 Flash for intelligent conversation and utilizes the native Windows SAPI (Speech API) for offline, zero-cost voice feedback.
An interactive Retrieval-Augmented Generation (RAG) web application that transforms any YouTube video into a live chat stream. Built with FastAPI, LangChain, FAISS, and Google Gemini, this tool indexes video transcripts in seconds and delivers precise, context-aware answers inside an authentic YouTube Dark Mode interface.
A finely tuned stack of technologies engineered for performance, deep learning, and scalable architecture.