RAG, agentic systems and enterprise data platforms — architected, delivered and defended in production. By the principal architect who built a 25-engineer Data & AI practice from zero and signed every SOW that left it.
The demo takes three weeks. Production takes an architecture: security, multi-tenancy, cost control, data pipelines that don't lie, and answers the business can trust. That second part is where programs stall — a proof of concept the board loved, a team strong in engineering but new to LLM behavior, a vendor proposal that reads well and ships nothing.
The missing piece is usually the same: one senior person who spans the data layer, the backend, the AI behavior and the commercial conversation — and who takes accountability for the outcome instead of the deliverable.
Four fronts, usually combined. The same person owns the architecture, writes and reviews the code, and answers for the result in front of leadership.
Retrieval-augmented generation and agent architectures on Azure OpenAI and LangChain — document intelligence, multi-tenant designs for regulated industries, GPT-4o realtime voice interfaces, prompt and context engineering that controls hallucination instead of hoping.
Delivered for banking and financial institutions where wrong answers have consequences.
Dataiku (full certification suite), Microsoft Fabric, Snowflake, Databricks. Reference architectures, metadata-driven ETL/ELT frameworks, star schemas that survive real data, incremental pipelines with change tracking.
Platform selection included — argued from delivery experience on all of them, not from a vendor deck.
The decisions before the code: platform versus protocol, governance, build-buy boundaries, data monetization, target architectures that a steering committee can approve and an engineering team can build.
Written to SOW grade — scope, deliverables and acceptance criteria a lawyer and an engineer can both read.
Building and running AI engineering organizations: hiring plans, training programs, Centers of Excellence, delivery processes — proven by building a 25-engineer practice across four countries from a standing start.
Or embedding into your existing team as the senior technical authority it's missing.
100% remote, in English, for clients primarily in North America and Europe. Most engagements start small — an audit or architecture review — and grow on results.
Long-running role inside your AI or data program: technical direction, reference designs, code review, delivery accountability. The senior spine of the team.
Weeks, not months: read the codebase, the pipelines and the team; report what is real, what is risk and what to do — in language both engineering and the board accept.
For AI initiatives that stalled between POC and production: honest diagnosis, re-architecture where needed, and delivery ownership until it ships.
For consultancies and vendors: solution architecture, proposal and SOW authorship, and the senior technical presence that closes late-stage enterprise deals.
A selection from the full project history — each row links to the detailed record.
The questions serious buyers ask — answered the way I'd answer them in the first call.
Both. I design the architecture and stay in the codebase — design reviews, key implementations, code review on the critical pieces. Architecture decisions land better when they're grounded in the code, not in a diagram.
Azure OpenAI, LangChain and Python for RAG and agentic systems, including GPT-4o realtime voice. Data platforms: Dataiku (full certification suite), Microsoft Fabric, Snowflake and Databricks. The choice is driven by your context, not my preference.
Yes — project recovery is a recurring pattern: read the codebase and the organization, diagnose honestly, re-architect what needs it, and carry delivery accountability until it ships.
A conversation through LinkedIn, then typically a scoped first step — an architecture review or delivery audit measured in weeks — before any large commitment. Remote, in English, primarily North America and Europe.
Tell me where it stands — even if the honest answer is “stuck.” The first conversation costs nothing and usually clarifies a lot.