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How to become an AI Solutions Architect

System design, model selection, build-vs-buy, and scaling and cost. Architect enterprise AI solutions that hold up in production.

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The AI Solutions Architect roadmap

Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.

Phase 1AI solution architecture foundations

Requirements gathering

An AI solution lives or dies on how well the problem was framed.

Feasibility and constraints

Feasibility is where ambition meets binding limits.

Stakeholder alignment

Product, security, and finance rarely want the same thing.

Phase 2AI solution architecture

Architecture patterns

Most AI systems fall into a few patterns: grounded single-pass RAG, tool-using agents, and orchestrated pipelines.

Model selection

There is no single best model, only the best model for a request.

RAG vs fine-tune vs prompt

Three ways to give a model the knowledge and behavior you need: prompt it, retrieve for it, or fine-tune it.

Phase 3Integration and data

Data architecture

Grounding an assistant means wiring it to the right sources the right way.

System integration

AI assistants rarely stand alone; they call CRMs, partner APIs, and internal services that are slow, rate-limited, or flaky.

Security and tenant isolation

In a multi-tenant assistant, one customer's data must never surface in another's answers.

Phase 4Scale and cost

Scaling and latency

A design that is fast in testing can fall over under peak load.

Cost optimization

AI spend is dominated by tokens and calls, and it grows quietly.

Caching strategies

Caching is one of the biggest cost and latency wins - and one of the easiest to get dangerously wrong.

Phase 5AI solution architecture governance

Reliability and fallbacks

Every dependency fails eventually, including your model provider.

Observability

You cannot improve what you cannot see.

Compliance and risk

In regulated domains, governance is not a follow-up.

Reading for this path

The primary sources behind the topics above, all free to read.

Coming from another job?

Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.

AI Strategist

Opportunity sizing, build-vs-buy, and adoption strategy. Decide where AI creates real business value.

AI Systems Engineer

GPUs, distributed training, kernels, and memory and throughput optimization. Make large models train and serve fast.

AI UX Designer

Human-AI interaction patterns, trust, uncertainty, and feedback loops. Design interfaces where people and models work well together.

Analytics Engineer

Data modeling, transformation pipelines, metrics, and data quality. Turn raw data into trusted datasets teams can build on.

Applied Scientist

Model adaptation, evaluation, and prototyping against real product problems. Take research from paper to production.

Computer Vision Engineer

Image and video models, VLMs, detection, and visual inspection. Ship systems that see and understand the world.

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The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.

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