AI systems and agent workflows
Model and tool orchestration, retrieval, structured output, evaluation, observability, approval gates and secure integration with existing systems.
How I build AI systemsI join teams where architecture has to become working software.
I take on design, implementation, production debugging and technical handover across AI, data and cloud systems. A project can start with a working architecture session or with code inside an existing team.
Data platforms
Architecture and implementation for batch, streaming, lakehouse, warehouse and analytics workloads, including release and operational ownership.
MLOps, DevOps and cloud architecture
Infrastructure as code, model delivery, containers, observability and production debugging across AWS, Google Cloud, Azure and Databricks.
Forward-deployed implementation
Hands-on architecture, coding and debugging alongside product, security, data and platform teams.
Analytics and executive BI
Source integration, governed models, semantic layers, dashboards, access control and maintainable reporting operations.
How I approach executive BITechnical education
Workshops, curriculum development and team instruction across data, AI, cloud and software delivery.
Ways to start
Initial fit assessment. A short conversation to understand the problem and decide whether I am the right person to help.
Paid architecture session. A working session that produces decisions, risks and next actions.
Hourly engineering. Implementation, debugging or technical leadership as the work changes.
Milestone delivery. Defined stages with agreed deliverables and acceptance criteria.