AI solution architecture & delivery
Bridge the gap between an AI idea and a system your team can operate. Architecture decisions, production ML and LLM/agent implementations from first sketch through handover.
What this role covers
Most AI projects stall between the promising prototype and the operating reality. An AI solution architect keeps both in view: the technical design and the team, budget, timeline and constraints required to make it real.
Validate whether an AI opportunity is technically feasible, economically defensible and organisationally deliverable before committing engineering headcount.
Discovery · technical diligence · build versus buy · risk framing · roadmap
Produce a concrete system design: data flow, model strategy, evaluation, integration points, cost model, security constraints and operating assumptions.
Solution architecture · PoC design · ML/LLM evaluation · cloud-edge trade-offs
Give senior technical direction to engineering teams while keeping business stakeholders aligned on what delivery actually requires.
Technical leadership · sprints · team enablement · stakeholder reviews
Leave behind production infrastructure, documentation and an internal team able to operate, extend and audit the system.
MLOps · LLMOps · documentation · workshops · ownership transfer
Selected architecture work
Representative engagements where architecture decisions determined whether an AI programme could move from concept to production.
Pan-European cloud-edge programme
IPCEI-CIS · sustainability workstream
A year-long IPCEI-CIS engagement designing a system that reasons over carbon intensity, infrastructure capacity and placement constraints across a distributed European compute environment. The architecture had to satisfy research, telecom and public-sector stakeholders while remaining implementable.
AI architecture · multi-agent design · implementation direction · Germany workshops · documented handover
Public construction-data platform
Public sector · construction and infrastructure
Solution architecture and technical discovery for a public-sector construction platform. The work translated a complex data and stakeholder landscape into an R&D plan that a client, technical partner and AI team could execute together.
Technical discovery · solution architecture · R&D planning · stakeholder alignment
Enterprise retail ML platform
Retail · enterprise analytics
Cloud infrastructure and production ML models delivered alongside the retailer's analytics team, followed by workshops so the internal team could operate and extend the environment without ongoing dependency.
Cloud MLOps · production models · knowledge transfer · customer workshops
Semiconductor incident resolution
Semiconductors · enterprise operations
An automated pipeline designed to classify and accelerate resolution of software incidents in a large US semiconductor environment, integrated with existing operations tooling.
Problem framing · ML pipeline design · enterprise integration
Ways to engage
BRAIN is a founder-led, Dubai-registered consultancy. Clients work directly with the architect throughout the engagement.
For a specific architecture decision, vendor choice or investment question.
Architecture review, model selection, build-vs-buy, roadmap, risk assessment.
For teams that need senior ownership before or instead of a permanent hire.
Technical direction, delivery oversight, team enablement, stakeholder alignment.
For an opportunity that needs to be designed, prototyped or moved toward production.
Discovery, architecture, working prototype, production plan, documentation and handover.
Start with the architecture question
Describe the decision, system or team you need help with. BRAIN replies with a clear sense of whether and how to proceed.
