AI / ML Engineer
I build AI systems: production RAG, secure LLMs, and the retrieval that makes them tell the truth.
Parveen, the Pleiades. Gulab, a rose. Born under Scorpius, the chart at right.
A production platform for building and governing intelligent multi-step AI workflows, combining LLM reasoning, structured knowledge retrieval, and external service calls in a single deterministic execution engine with role-scoped governance.
A production retrieval-augmented generation service that matches enterprise support queries against technical documentation, selects the most relevant documents using LLMs, and generates precise answers, deployed as a containerized microservice with multi-protocol streaming.
Master's thesis at RPTU Kaiserslautern: an end-to-end system that translates open-vocabulary text into expressive 3D American Sign Language motion, including body, hands, and facial expressions, using VQ-VAE motion encoding and a GPT-based autoregressive transformer.
A2A solves how agents talk to each other. It doesn't solve how they stay pointed at the same outcome. Each agent can do its job correctly and the system still produces the wrong result.
Model Context Protocol gets described as a way to give LLMs access to tools. That framing buries the harder question: how does a system decide what a model is allowed to touch?