AI reasoning & retrieval
RAG, embeddings, semantic search, reranking concepts, LLM integration, evaluation and hallucination awareness.
I’m interested in the entire path from a good idea to a usable intelligent product.
“I chose AI engineering because I want to build intelligent systems that solve real problems instead of creating isolated demos. I enjoy combining AI reasoning, RAG and agents with complete full-stack products that people can actually use.”
Retrieval, agents, application services and the interfaces around them.
RAG, embeddings, semantic search, reranking concepts, LLM integration, evaluation and hallucination awareness.
LangGraph, multi-agent workflows and prompt engineering.
React, Next.js, TypeScript, Python, FastAPI, ASP.NET Core Web API and REST APIs.
Docker, Git, Linux / WSL, API boundaries, PostgreSQL, Qdrant and Redis familiarity.
I want retrieval, agents, backend APIs and frontend interfaces to work as a complete product. Reliable answers need evidence, clear boundaries and an experience people can use.
My direction is full-stack AI engineering: RAG applications, agent workflows and the services and interfaces around them.
My coursework includes in AI, Machine Learning, NLP, Data Structures & Algorithms, Databases and Parallel & Distributed Computing.
Explore the blog ↗Docker, Git, Linux / WSL, API boundaries, PostgreSQL, Qdrant and Redis familiarity. GitHub Actions and Async Python are also part of my learning and development stack.
Uses ↗Open to AI Engineer jobs, internships and selected freelance opportunities. Remote and international opportunities preferred.
Hire Me ↗