What the job involves day to day
You take a business problem and turn it into a system design someone can build. That means choosing a model and the platform that serves it, deciding how it connects to existing applications and data, and planning for scale, failure and spend. AWS's guidance for its own architects lists choosing the right model for each use case, handling API integration, securing access and tuning performance as core skills. You draw the diagrams, write the design documents, and defend the tradeoffs in review.
Security and cost take up more of the week than newcomers expect. You set who can call which model, encrypt data at rest and in transit, and map the design to the rules the customer must follow. On cost, you design autoscaling for spiky inference traffic and pick cheaper compute where it fits. You also plan high availability across zones or regions, backups and failover, because an AI feature in production gets the same uptime questions as any other system.
How it differs at cloud vendors, consultancies and in-house
At a cloud vendor, the role is largely pre-sales and adoption. You work with many customer accounts, run architecture sessions and proofs of concept, and design on your employer's own services. Depth on one platform matters most, and your success is tied to customers building and running workloads there. Expect travel, demos and a quota-carrying sales partner.
At a consultancy or systems integrator, you scope work, estimate effort and design the solution the delivery team will build, often across several vendors. You write proposals and statements of work as well as architecture. In-house, you serve internal product and platform teams. There is less selling, and more ownership of standards, reference designs, vendor reviews and the long-term cost of what gets built. You live with your decisions for years.
Skills that carry the role
The base is general cloud and software architecture: APIs, serverless and container platforms, event-driven design, identity and networking. On top of that sits AI-specific knowledge such as model selection, retrieval and data pipelines feeding models, evaluation, and the cost profile of inference. AWS notes that AI workloads need knowledge beyond traditional cloud architecture, including service configuration and optimization strategies specific to generative AI.
The other half is communication. You gather requirements from people who are not engineers, explain tradeoffs to executives, and give implementation teams guidance they can act on. O*NET's profile for computer systems architects, which lists solutions architect as a reported title, puts talking with staff or clients about requirements and recommending components near the top of the task list.
Certifications and who they help
No certification is required to do this work, and AI work has no license. Vendor certifications help most when you sell or deliver on that vendor's platform, which is common at cloud vendors and partners. Microsoft offers the Agentic AI Business Solutions Architect Expert certification. To earn it you pass exam AB-100 and hold at least one listed Associate certification, such as Power Platform Developer (PL-400) or Power Platform Functional Consultant (PL-200).
Microsoft lists no retirement date for AB-100 and prices it by the country where you take it. The English version of the exam will be updated on November 16, 2026, and localized versions about eight weeks later, so check the AB-100 study guide for the skills measured after that date. A certification shows platform knowledge. It will not stand in for shipped designs when a hiring manager asks what you have built.
Pay
USD a year, before tax. US figures. BLS rows: OEWS May 2025, moved to 2026 with the Employment Cost Index, for computer occupations, all other. BLS has no AI series. This is the nearest series, not an exact match, and it covers the whole occupation, not AI specialists alone. Job ad rows: midpoints of US dollar pay ranges in 27 live AutomationRoles.ai ads from 18 employers on 5 November 2026. These ads lean toward large AI and technology employers, and base pay in an ad leaves out stock and bonus. They are not official figures or a market average. Refer to the AI Solutions Architects salary guide for figures from live adverts.
What you need
- Cloud architectureWorking knowledge of APIs, serverless and container platforms, identity, networking and event-driven design on at least one major cloud.
- AI system designModel selection, integrating models with enterprise applications and data pipelines, and evaluating output quality.
- Security and governanceFine-grained access control for models, encryption in transit and at rest, and designing for the regulations a customer must meet.
- Cost and reliabilityAutoscaling for variable inference load, compute choices that cut spend, and multi-zone or multi-region failover.
- Client communicationGathering requirements from non-engineers, presenting tradeoffs, and writing guidance implementation teams can follow.
- Optional certificationVendor architect certifications such as Microsoft AB-100 help mainly where you work on that vendor's platform.
How to get there
- Early careerBuild software or cloud depthWork as a software, cloud, data or ML engineer and ship systems that run in production.
- Early to mid careerAdd AI system workBuild features on hosted models: retrieval, integration with existing apps, evaluation and cost tracking.
- Mid careerTake on design ownershipWrite design documents, lead architecture reviews and own security and cost decisions for a project.
- Mid careerMove into a customer-facing or architect titleJoin a cloud vendor, partner or consultancy as a solutions architect, or become an internal AI architect.
- SeniorSpecialize or leadGo deep in an industry or platform, or lead a team of architects and set reference designs.
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Questions people ask
Is an AI solutions architect a sales job?
At cloud vendors and many software companies, much of the role is pre-sales: designing proofs of concept and helping customers adopt the platform. At consultancies it mixes scoping and delivery design. In-house roles involve little selling.
How is it different from an AI engineer?
An AI engineer mostly builds and ships the system. The architect decides how the pieces fit, which models and platforms to use, and how security, cost and reliability are handled, then guides the builders. Many architects were engineers first.
Do I need a certification?
No. AI work has no license, and no certification is required. Vendor certifications such as Microsoft's Agentic AI Business Solutions Architect Expert, which requires exam AB-100 plus an Associate certification, help most when you work on that vendor's platform.
Why are the BLS and job ad pay figures so far apart?
BLS has no AI series, so the BLS row covers all of computer occupations, all other, a broad mix of IT roles. The job ads lean toward large AI and technology employers. Both are US figures, and base pay in an ad leaves out stock and bonus.
What does an AI solutions architect worry about most?
Usually security, cost and reliability once a design leaves the demo stage. That covers who can access which model and data, how inference spend grows with traffic, and how the system behaves when a zone or region fails.
Sources
- AWS Training and Certification Blog: 7 essential generative AI concepts for solutions architects
- Microsoft Learn: Agentic AI Business Solutions Architect Expert certification
- Microsoft Learn: Exam AB-100
- O*NET OnLine: Computer Systems Engineers/Architects 15-1299.08
- O*NET OnLine: Computer Occupations, All Other 15-1299.00
- BLS OEWS May 2025: 15-1299 Computer Occupations, All Other
