DEUS

AI Engineering and Data Engineering work focused on LLM/RAG systems, autonomous agents, and production-grade data workflows for enterprise clients.

At DEUS, I work at the intersection of applied GenAI and data engineering, helping enterprise teams move from pilots to production. My focus is on designing robust LLM systems, integrating external tools through MCP, and building the data foundations needed for reliable AI products.

My work spans end-to-end delivery: from retrieval architecture and agent design to data-layer modeling and quality controls. The goal is always the same: practical solutions with clear business impact, low operational friction, and production-ready reliability.

Technologies

Python

LangGraph

LangChain

Langfuse

Docker

Git

SQL

Spark

Azure

AzureAI

Databricks

Fabric

Data Factory

Cosmos DB

OpenAI

Claude

MCP

Ollama

Pre-commit

Gemini

Key Achievements

Architected RAG and autonomous agent solutions using MCP to integrate enterprise tools and compliance-oriented workflows.
Reduced query latency by up to 80% in complex retrieval pipelines through optimized vector search and agentic orchestration.
Delivered a high-throughput compliance chatbot with scalable batching patterns for peak demand scenarios.
Built medallion-style data layers (Bronze/Silver/Gold) and enforced data quality checks across ingestion and transformation stages.
Established CI quality gates with linting and best-practice checks to improve reliability and maintainability.

Highlights

LLM and RAG architecture for enterprise use cases
Agentic workflows with MCP and tool integration
Data platform engineering with Databricks and Spark
Production-focused quality and operational excellence

Company Website

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