I am an Artificial Intelligence & Machine Learning intern with a CGPA of 8.0. I design and deploy end-to-end predictive pipelines, self-correcting automation logic, and hardware-integrated data intelligence systems.
I am an Artificial Intelligence & Machine Learning undergraduate passionate about building intelligent systems that solve real-world problems through data, algorithms, and automation. My interests span machine learning, deep learning, predictive analytics, natural language processing, recommendation systems, and intelligent software development.
I enjoy transforming complex datasets into meaningful insights and developing scalable solutions that combine analytical thinking with practical engineering. Through academic and personal projects, I have gained experience in designing machine learning models, developing AI-powered applications, and building end-to-end software systems.
I am continuously exploring emerging technologies in AI and data science while strengthening my problem-solving, research, and software engineering skills to create impactful and innovative solutions.
An edge-integrated LSTM forecasting pipeline built to monitor multi-variate environmental datasets.
View Architecture & CodeAn end-to-end regression application mapping geographic indices and structural features.
View Architecture & CodeAn intelligent neural network client executing intent token classification patterns.
View Architecture & CodeAn intelligent document analysis platform utilizing Retrieval-Augmented Generation (RAG) to dynamically query and summarize academic papers.
View Architecture & CodeAdvanced financial classification architecture engineered around transaction validation logs.
View Architecture & CodeA full-stack analytics platform that queries structured databases and documents using natural language and local LLMs.
View Architecture & CodeA hybrid optimization engine deploying user matrix mappings and KNN logic.
View Architecture & CodeContributing directly within an engineering development team to build predictive edge-monitoring data platforms and time-series pipelines. Deployed operational Chemical Transport Models (CTM) using operator splitting matrices to secure optimization benchmarks.
Skills Demonstrated: Leadership, Event Management, Stakeholder Coordination, Problem Solving, Operations Management
Skills Demonstrated: Marketing, Communication, Team Collaboration, Event Promotion, Community Engagement
Anthropic
Simplilearn
AlgoUniversity
NPTEL
TATA iQ Job Simulation (Forage)
Graduation Year: 2027