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