Guided program · Architecture track
This program is for experienced software and infrastructure professionals who want to move toward Cloud/AI Solutions Architect work. It starts with the same AI/ML and agentic-tools fluency as the FDE path, then goes deeper into architecture judgment: workload shape, data, identity, cost, resilience, oversight, and the decisions a model cannot safely make on its own.
Apply / book a callCloud experience is valuable, but it is not enough to bolt an AI label onto an old infrastructure diagram. The shared spine builds a working model of AI systems first: how models behave, how retrieval and tools change the system, and how to evaluate and secure the result.
You should already be comfortable with software and deployment concepts. Your cloud depth can vary; the program makes the AI foundation common while giving the architecture tail enough room to examine trade-offs rather than only follow a service tutorial.
Program shape
Plan for roughly 4–6 months part-time. The final architecture work is paced around your starting depth and the complexity of the capstone.
Stage 1
Learn what models compute, how cost and latency affect design, and how to direct AI coding and agentic tools with context, constraints, and machine-usable output.
Stage 2
Design retrieval and grounding that can be tested, cited, and debugged rather than trusting a demo that happens to look convincing.
Stage 3
Reason about tool use, protocols, memory, orchestration, and the boundaries where an agent needs supervision or a deterministic workflow.
Stage 4
Make evaluation, guardrails, observability, responsible AI, and security part of the architecture instead of afterthoughts.
Stage 5
Diverge into workload shapes, reference architectures, scaling, data architecture, and cost decisions that you can defend to a technical and business audience.
Stage 6
Design identity, approval, isolation, and auditability into the system, then present an end-to-end architecture with a clear decision record.
The work begins with requirements and constraints. You will compare retrieval, agentic, and workflow shapes before reaching for a cloud product.
A useful architecture explains cost, latency, resilience, data boundaries, identity, and operational ownership to more than one kind of stakeholder.
Approval workflows, isolation, auditability, and guardrails are design material. Autonomy is not the same thing as a safe system.
The capstone becomes a design record: assumptions, alternatives, decisions, risks, and the evidence that supports the chosen path.
The AI-first framing is new. We will publish alumni stories only when the underlying outcome is verified and the person has consented to the use of their name or details.
No single job title is required. Existing experience in software, backend, QA, DevOps, or adjacent technical work is useful. Cloud depth can vary; the program will not assume that every participant has the same infrastructure background.
No. It is a guided architecture program. Cloud services and infrastructure are used as the setting for decisions about AI systems, not as a list of badges to memorise.
The realistic public estimate is 4–6 months part-time. Your exact schedule depends on your starting point, available weekly time, and the scope of the capstone.
Guidance-based pricing — talk to us. We discuss fit, background, and delivery shape before quoting rather than placing a fixed shopping-cart price on an adaptable professional program.