Four ways to industrialize AI in the cloud
From strategy to production, I design agentic AI, MLOps and AWS architectures that move beyond demos and become reliable business systems.
Global, remote-first delivery
Built for async collaboration with teams in Europe, North America and Asia-Pacific. Operational from project framing to production delivery.
AI Agent Engineering
Agents that use tools, data and workflows
Custom agentic systems for RAG, multi-agent orchestration, tool use, MCP-style integrations and production deployment on your preferred cloud stack.
- RAG and knowledge workflows
- Tool calling and orchestration
- Production deployment patterns
Cloud & MLOps Architecture
From ML experiments to production systems
Serverless AWS architectures, MLOps pipelines, observability, cost optimization and infrastructure patterns for AI systems that must scale safely.
- AWS serverless and event-driven systems
- MLOps pipelines and deployment automation
- Observability, reliability and FinOps
Document AI & Intelligent Automation
Turn document operations into structured data
OCR, classification, extraction, summarization and automation for organizations handling contracts, reports, claims, invoices or regulated documents.
- Text, OCR and multimodal extraction
- Classification and validation workflows
- Human-in-the-loop automation
AI Strategy & Technical Advisory
Know what to build before you build it
Technical audits, feasibility reviews, architecture roadmaps and execution plans for CTOs, founders and innovation teams evaluating AI initiatives.
- AI stack and architecture audit
- Build-versus-buy and risk assessment
- Budgeted execution roadmap