I am passionate about building software at the intersection of AI/ML, backend engineering, and scalable data systems.
My work focuses on:
- designing practical machine learning applications
- building end-to-end data and backend workflows
- developing clean, user-facing products with strong engineering fundamentals
AI-powered disaster response system that combines YOLOv8-based survivor detection with NLP-driven emergency message classification to improve rescue prioritization and situational awareness.
Built a scalable data engineering pipeline using Airflow, Kafka, Spark, Cassandra, PostgreSQL, and Docker for real-time ingestion, stream processing, and distributed storage.
LLM-powered content generation engine that uses structured style deconstruction, JSON-based prompts, and few-shot learning to generate high-fidelity professional content.
Machine learning application built with Python and Streamlit that recommends similar movies using content-based filtering and integrates with the TMDB API.
Python-based task management system with CLI, Tkinter GUI, and FastAPI backend, supporting CRUD operations, search, validation, and persistent JSON storage.
Implemented a neural network from scratch in Python to solve the XOR problem, with an interactive Streamlit-based interface for demonstration.
Languages: Python, C++, JavaScript, TypeScript, SQL
AI / ML: Scikit-learn, NLP, Computer Vision, Prompt Engineering, Few-Shot Learning
Frameworks / Tools: FastAPI, Streamlit, React, Docker
Data Engineering: Airflow, Kafka, Spark, Cassandra, PostgreSQL
Core Areas: Machine Learning, Backend Development, Data Pipelines, Full-Stack Development
- building projects that solve real problems
- writing clean, maintainable, and scalable code
- continuously improving across software engineering and machine learning
- LinkedIn: aman0910