I’m not your average AI engineer. I’d rather be experimenting with new architectures or deploying scalable models than chasing frameworks that go out of trend every month. Most days, you’ll find me fine-tuning LLMs, integrating RAG pipelines, or building autonomous AI agents that actually solve real problems.
I build intelligent systems from the ground up. From data pipelines to deployment, I work across the full AI lifecycle—designing, training, and shipping models that bring automation to life. I care less about buzzwords and more about creating AI solutions that feel seamless, smart, and human.
Intelligent Systems & Applied AI Work
Built Medora AI I engineered AI automation pipelines using LLMs and agentic workflows. Focused on scalable RAG and vector search systems, I helped design real-time retrieval pipelines and multi-agent orchestration for domain-specific automation.
Built the RAG-based Patent Innovation Researcher — a fully modular retrieval-augmented generation framework capable of extracting and analyzing innovation data from complex patent documents using semantic search, embeddings, and multi-step reasoning.
Developed Vehicle Insurance Data Pipeline — an end-to-end machine learning data pipeline with real-time monitoring using MLflow, DVC, and Grafana dashboards. Automated preprocessing, model training, and deployment workflows integrated with FastAPI microservices.
Contributions include developing custom GPT-style language models, implementing transformer architectures from scratch, and exploring emerging AgentOps stacks with LangGraph, CrewAI, and AutoGen for intelligent automation and adaptive task routing.
Focused on building 0→1 intelligent systems — from experimentation to production. My workflow combines MLOps, backend AI infra, and automation to ship real-world AI products that learn, adapt, and scale.
GitHub Contributions ● @extremecoder-rgb
Stack I use
Technologies I work with to build products that solve real problems
If you've read this far, you might be interested in what I do.
