AI & Machine Learning
- Generative AI
- LLM Applications
- RAG Architecture
- Autonomous Agents
- LangChain
- Prompt Engineering
- Vector Embeddings
- Semantic Search
- Computer Vision (OpenCV)
- Whisper & Multimodal AI
Systems Architecture & Philosophy
Production AI applications require more than static API wrappers. They demand robust schema normalization, low-latency client state, multi-model fallback redundancy, and verified execution proof.
I build end-to-end software systems that combine mathematical AI foundations with high-velocity product execution.
My portfolio covers document extraction, video question answering, computer vision and client-side web tools. Project pages describe the available implementation, screenshots and links. Benchmarks are included only when their methodology is documented.
Schema validation helps check output shape. It does not guarantee factual accuracy; source review remains necessary.
Client-side state and browser persistence support local workflows. Storage should not be assumed encrypted or suitable for sensitive data.
Python and OpenCV support video and image workflows. Performance depends on hardware, input and configuration.
An illustrative guide to the portfolio workflows. This is not a running AI service, model comparison or benchmark.
Trade-off: Complex layouts and uncertain fields need human review.
Architectural Arsenal
Technologies, frameworks, mathematical foundations, and AI toolchains utilized to engineer scalable intelligent applications.