I am a Computer Science Engineering student and software engineer with hands-on experience building full-stack applications, AI/ML systems, and scalable backend services. My work spans software development, generative AI, retrieval-augmented generation (RAG), machine learning, and system design.
I have experience with Python, C++, JavaScript, SQL, React.js, Flutter, FastAPI, Node.js, PostgreSQL, Redis, MongoDB, FAISS, LangChain, Docker, Nginx, Azure, GCP, Linux, and GitHub Actions.
During my software engineering internship at Unisys, I worked on production-oriented software, including an interactive call-graph visualization platform built with React and FastAPI. I worked with legacy code analysis, GraphViz/DOT, NetworkX, REST APIs, JWT authentication, Nginx, and Linux-based deployment.
I have also built AI-powered systems such as SupportRAG, a retrieval-augmented support platform using FastAPI, LangChain, FAISS, and Hugging Face, with a distributed architecture involving PostgreSQL, Redis queues, background workers, load balancing, and automated job recovery. Other projects include agentic AI assistants, voice-based applications, generative AI tools, and ML systems for healthcare applications.
My experience also includes research and applied machine learning, particularly speech processing and deep learning. I have worked with models such as HuBERT, Wav2Vec2, XGBoost, and CNN-GRU architectures, along with feature engineering, model evaluation, explainability using SHAP, and experimentation across multiple datasets.
I help clients solve problems such as:
• Building web and mobile applications from idea to deployment
• Developing AI assistants, RAG pipelines, and LLM-powered applications
• Designing scalable APIs and backend architectures
• Integrating databases, queues, caching, authentication, and external APIs
• Automating workflows and improving existing software systems
• Developing and evaluating machine-learning models
• Debugging, optimizing, and modernizing existing codebases
I focus on building practical, maintainable solutions rather than prototypes that only work in ideal conditions, with an emphasis on clean architecture, performance, reliability, and usability.