Two disciplines. One ship-it-Friday approach.
Data Engineering builds the foundations. AI & Machine Learning builds on top. Together they ship things that don't quietly die in Q3.
Data Engineering
No house without a foundation. I build data platforms that engineers trust, business understands, and pagers don't wake up about at 2 AM.
Foundations
A solid data foundation: governance, modeling, and architecture that grows with your organization instead of fighting it.
Data Platforms
Reliable ETL/ELT in batch or streaming. Idempotent, observable, and documented enough that the next person doesn't cry.
Data Integration
Lakehouse architectures on AWS, Azure or Databricks that scale with your volume — not your bill.
Lakehouse setups
Tests, contracts, and monitoring so dashboards, models, and decisions stay trustworthy.
AI & Machine Learning
From proof-of-concept to production. AI systems that are predictable, monitorable, and earn back their cost — with receipts.
LLM applications
Production-ready integrations with Azure OpenAI, Anthropic, and open-source models — pragmatic and cost-aware.
RAG Systems
Retrieval-Augmented Generation on your own knowledge base: vector store, evaluation, and feedback loops included.
Agentic systems
Smart agents and workflows that take repetitive knowledge work off your team's plate — measured in hours saved.
Got a concrete problem in mind?
A short audit or strategy session usually gives surprising clarity. No commitments, no decks full of stock photos.