//
Current Focus
- ▸Deepening expertise in cloud platforms, with a particular emphasis on AWS/Azure for scalable and efficient AI and analytics solutions.
- ▸Progressing through MLOps-focused courses, emphasizing the deployment, monitoring, and optimization of machine learning models in production.
- ▸Exploring tools and techniques for efficient data preprocessing, pipeline automation, and integration with AI systems.
- ▸Experimenting with cutting-edge techniques in generative AI, including fine-tuning models for real-world applications.
- ▸Engaging in team-based initiatives to apply AI and machine learning principles to solve industry-relevant problems.
- ▸Developing agentic AI solutions that leverage generative models for autonomous task execution, dynamic tool use, and adaptive reasoning.
//
- ▸Developed an agentic AI system using LangGraph and Azure OpenAI to automate extraction and classification of construction product specifications from unstructured documents, reducing manual review time by 40%.
- ▸Built a multi-agent orchestration pipeline for intelligent document parsing, leveraging RAG with vector search to answer technical queries across 10,000+ product datasheets.
- ▸Designed and deployed a Streamlit-based internal tool for quality engineers to query product compliance data in natural language, increasing team productivity by 25%.
- ▸Implemented CI/CD pipelines with Docker and GitHub Actions for model versioning and deployment to Azure, ensuring reproducibility and scalability across environments.
//
Built a CNN model using U-Net architecture for segmenting satellite images using TensorFlow and Keras. Deployed on Hugging Face for real time prediction.
Developed as part of Llama-Impact Hackathon. Solves the problem of using government websites easier for common people, navigating and providing legal advice.
A forecasting pipeline that retrieves real-time Bitcoin price data via API, preprocesses it, and trains an LSTM model to predict future price trends. Orchestrated with Apache Airflow.
Built an end-to-end ML pipeline to predict surge pricing for Uber and Lyft rides using historical ride, weather, and event data. Leveraged MLOps principles with MLflow, DVC, and Docker.
An agentic AI career guidance system using LangGraph and multi-agent orchestration to provide personalized career recommendations, skill gap analysis, and learning path generation.
An AI-powered travel planning assistant using multi-agent workflows to generate personalized itineraries, recommend destinations, optimize budgets, and provide real-time travel insights.