Data & AI Architect
I architect and deploy production-ready AI systems.
RAG, LLM systems, MLOps, data platforms, governance & security - from executive scoping to deployment.
Independent. Enterprise-grade. End-to-end execution.
Enterprise-grade delivery
- •End-to-end AI architecture
- •Secure data pipelines & governance
- •Production-grade LLM systems (RAG, agents)
- •Cloud-native deployment
- •Executive alignment (CIO/CTO/Head of AI)
Availability
Available for AI consulting mandates (scoping, architecture, delivery).
Also open to senior roles: AI Architect / AI Platform Lead / AI Product Leadership.
Trust signals
Method, governance and delivery discipline - no marketing claims.
Delivery discipline
- Scoping: goals, constraints, risks, acceptance criteria
- Architecture: ADRs, reviews, standards, threat model
- Run: observability, incidents, SLO/SLI, post-mortems
Security & governance
- RBAC / least-privilege, audit trails
- Data classification & retention
- LLM safety: prompts, evals, light red teaming
- Compliance: privacy-by-design (when applicable)
Artifacts (on request)
- Statement of Work (SOW) / delivery plan
- Architecture diagram + runbook
- Risk register + DPIA checklist
- Cost model (FinOps) & capacity assumptions
Documents and examples available on request.
What I do
A value-focused approach from strategy to production delivery.
Data Engineering
Reliable pipelines, data quality, controlled costs. From source to value, cloud or on-prem.
- •Build robust, observable pipelines
- •Model data for business analysis and decision-making
- •Improve data quality and flow governance
Outcomes
AI Engineering
From POC to AI product: LLMs, RAG, agents, evaluation, MLOps. Focus on robustness and security.
- •Integrate ML/LLM models into operational workflows
- •Set up core MLOps practices for reliability
- •Design AI use cases with clear user impact
Outcomes
Product & Architecture
Target architecture, tech choices, scalable patterns. Align tech, business, and constraints (privacy, security).
- •Shape architecture around real product and business needs
- •Drive technical choices with maintainability in mind
- •Structure product and data building blocks coherently
Outcomes
Strategy & Delivery
Framing, roadmap, MVP, iterative delivery. Pragmatic leadership to production and beyond.
- •Prioritize data and AI initiatives around business constraints
- •Build a roadmap focused on measurable impact
- •Track decisions and execution with clear ownership
Outcomes
Featured projects
A selection of recent work.
Reference project
Short summary of the project, context and key outcomes.
Another project
Short project description.
Blog & insights
Thoughts on data, AI and product architecture.
Media & community
Content, sharing and conversations across networks.
