What AI Solutions Architects Build and Why Engineering Teams Need Them Now
An AI solutions architect turns a business problem into a system design that engineers can build, operators can run, and auditors can inspect. The role covers three layers that used to carry separate titles: solution architecture for one programme, technical architecture for the platform and code patterns beneath it, and enterprise architecture for the multi-year roadmap across systems. Scrums.com places one accountable architect across all three, forward deployed into your teams.
The work has changed. The hard questions about microservices, event streams, and cloud accounts remain. On top of them, every enterprise now needs an answer for where large language models and agents sit in the estate: which model gateway, which retrieval layer, which tool-calling boundaries, and which data may reach a model at all. McKinsey's 2025 State of AI survey reports that 88 percent of organisations use AI in at least one business function, but only 23 percent are scaling an agentic system anywhere in the enterprise. That gap is an architecture problem.
Integration is the constraint. MuleSoft's 2025 Connectivity Benchmark puts the average enterprise at 897 applications, with only 29 percent integrated, while 94 percent of IT decision-makers plan to deploy autonomous agents within two years. An agent that cannot reach the core system safely has no value. The architect designs the API layer, event contracts, identity model, and approval steps that let it act.
Cloud foundations still decide delivery speed. Gartner forecast worldwide public cloud end-user spending of $723 billion in 2025. Landing zones and policy guardrails set up in the first month decide whether the tenth team ships in a week or waits a quarter. Scrums.com architects deliver these foundations as code, managed through the Scrums.com Enterprise AI Platform for Software Engineering: shortlist in 48 hours, first commit inside three weeks. See the AI agent platform and AI automation delivery.
Essential Skills to Look For in an AI Solutions Architect
Architecture titles are easy to claim. The competencies below separate architects who have shipped systems from those who have only drawn them.
Distributed systems design. Service decomposition by domain, synchronous versus asynchronous integration, idempotency, back-pressure, and failure modes. Strong candidates design with Apache Kafka or an equivalent event backbone, REST and GraphQL APIs, and Kubernetes as the runtime, and can explain when a modular monolith is the better answer.
Cloud platform depth on AWS or Azure. Expect working knowledge of the AWS Well-Architected Framework and Azure landing zones: account structure, identity, network segmentation, policy as code, cost allocation, and Terraform. Test multi-cloud claims against real production work on at least one provider.
Enterprise language runtimes. Most estates run on Java and Spring, .NET Core, and Node.js. An architect needs enough fluency to review a pull request, set coding and observability standards, and judge framework choices.
Data and integration architecture. Canonical data models, master data ownership, change data capture, data lineage, and residency and retention design for regulated sectors.
The AI-era additions. Model gateway and routing patterns, retrieval-augmented generation design, vector store selection, agent runtimes with tool registries and human-in-the-loop checkpoints, evaluation datasets and tracing, token cost modelling, and the OWASP Top 10 for LLM applications. Architects also need a position on AI-assisted engineering. The 2025 DORA report finds that 90 percent of respondents use AI at work and that AI adoption still has a negative relationship with delivery stability. The architect sets the review gates and platform standards that keep speed from becoming incidents.
Communication and decision records. Clear C4 diagrams, written architecture decision records, and the ability to present trade-offs to a CFO and a platform engineer alike.
Where AI Solutions Architects Deliver Measurable ROI
An architect's value shows up as avoided rework, faster onboarding onto shared platforms, and controls that pass audit the first time.
FinTech and banking. Core banking, payments, and lending stacks carry decades of point-to-point integration. Architects define the API and event layer that lets new channels and AI assistants reach account, transaction, and KYC data without another direct database connection. The same design makes each model call traceable to the data it saw, which is what compliance functions under DORA in the EU and FCA operational resilience rules ask to see. Outcomes are fewer integration incidents, shorter change lead time, and one reviewed pattern for agent actions such as dispute triage.
Insurance. Policy administration and claims systems are often the oldest in the estate. A target-state architecture that sequences the strangler migration, standardises document ingestion, and places an LLM extraction and summarisation service behind a governed API lets carriers modernise claims intake without a full core replacement. The roadmap turns a multi-year programme into quarterly releases with measurable cycle-time reduction.
SaaS. Product companies need multi-tenant isolation, cost-per-tenant visibility, and a path to embed AI features without doubling cloud spend. Architects design tenant boundaries on Kubernetes, tiered model routing so that cheap models serve high-volume requests, and evaluation pipelines that stop regressions before release. Outcomes are gross-margin protection and a faster feature cadence.
Public sector. Government and health bodies face data residency, accessibility, and procurement constraints. A landing zone built on Azure landing zone design areas or the AWS Well-Architected Framework gives departments a compliant, repeatable platform, and an integration architecture over legacy case-management systems gives citizen-facing services and AI assistants controlled access to records. The measurable result is reduced platform duplication and audit findings closed at design time rather than after go-live.
Solutions Architect vs Technical Architect vs Enterprise Architect vs Cloud Architect: Which One Do You Need?
Buyers post one of four titles for the same need. The distinctions below follow the industry framing summarised in LeanIX's comparison of architect roles, with the AI-era responsibilities added.
Solutions architect. Owns the design of one solution or programme end to end: requirements, system context, integration, data flows, non-functional requirements, and build sequence. The right hire when a defined product, migration, or AI initiative must ship and integrate with the estate.
Technical architect. Goes one level deeper on a specific stack: service boundaries, framework and library choices, coding standards, performance, and the runtime on Kubernetes or a managed platform. Reviews code and pairs with senior engineers. Hire this profile when the design exists and the risk is implementation quality, or when a Java, .NET, or Node.js platform needs a technical lead.
Enterprise architect. Works at portfolio level over a multi-year horizon: capability maps, application inventories, target-state architecture, standards, and the roadmap that decides what to retire, replace, or retain. Often uses TOGAF or a lighter method. Hire this profile before a large modernisation or AI investment when leadership needs a defensible plan.
Cloud architect. Specialises in the platform layer: landing zones, identity, network, policy as code, cost management, and migration patterns on AWS or Azure. Hire this profile when the foundation is missing or unsafe, or when regulated workloads must migrate under audit.
How Scrums.com handles the overlap. One AI-certified architect covers the solution and technical layers on a programme, and scales up to roadmap work or down to landing zone build as the engagement demands. The platform records decisions, standards, and delivery metrics so the work survives any change in personnel. If you are unsure which title fits, start a conversation and describe the outcome you need.
What AI Solutions Architects Cost: US, UK, and Africa Benchmarks
Architects sit at the top of the engineering pay scale in every market, and AI platform experience adds a premium.
United States. ZipRecruiter data as of August 2026 puts the average annual salary for a solutions architect at $145,963, with most salaries between $126,000 at the 25th percentile and $166,000 at the 75th percentile, and the 90th percentile at $183,000.
United Kingdom. Glassdoor UK data as of May 2026 reports an average solutions architect salary of £79,315, with a typical range of £63,321 to £100,989. In London the average rises to £88,144.
Africa. Glassdoor South Africa reports an average solution architect salary of ZAR 875,000 per year, with a typical range of ZAR 740,000 to ZAR 1,300,000. CareerLead's 2025 Africa salary guide places lead and principal engineers at $65,000 to $95,000 or more in South Africa, $48,000 to $70,000 or more in Kenya, and $38,000 to $65,000 or more in Nigeria, with cloud architects across the region at $40,000 to $85,000. The saving against US and UK rates is significant, and vetted talent is the same calibre.
The hidden costs of hiring directly. Recruiter fees, a search that runs for months, employer taxes and benefits, and the risk that one architect leaves with the design in their head.
The Scrums.com model. Scrums.com places AI-certified architects from a pool of over 10,000 pre-vetted engineers across the US, UK, and Africa, forward deployed into your systems and managed through the Scrums.com Enterprise AI Platform for Software Engineering. You get a shortlist in 48 hours, a first commit inside three weeks, and decisions recorded on the platform rather than in one person's memory. Start a conversation for a proposal shaped to your programme.
How AI Solutions Architects Work Inside a Forward-Deployed, Platform-Managed Team
Architecture only creates value when it changes what gets built. Scrums.com places the architect inside your environment and holds the work to the same delivery metrics as the engineers.
Weeks one to three: discovery and first commit. The architect reads your code, infrastructure, and incident history before drawing anything. First outputs are a system context diagram, an inventory of integrations and data stores, and a risk list. The first commit is usually a decision record, a Terraform module, or an API contract, merged through your normal review process.
Architecture as code, not slides. Target designs are expressed as C4 models in the repository, architecture decision records next to the code they govern, reference implementations for each approved pattern, and policy-as-code guardrails in the platform. Engineers meet the architecture through pull request templates and CI checks, not a document they have to find.
LLM and agent platform patterns. For AI workloads the architect standardises a model gateway with routing, rate limiting, and cost attribution; a retrieval service with document classification and access control; an agent runtime with typed tool contracts, idempotent actions, and human approval steps for anything that moves money or changes records; and an evaluation and tracing pipeline that runs in CI. Each new AI use case reuses these shared services instead of rebuilding them.
Modernisation in increments. Strangler migrations run behind an API gateway or event backbone on Kafka, with each slice measured for latency, error rate, and cost before the next one starts. Landing zones and Kubernetes platforms ship with a paved road that teams adopt because it is faster.
Managed through the platform. The Scrums.com Enterprise AI Platform for Software Engineering records decisions, standards, delivery metrics, and AI usage across the team. Leadership sees architecture progress beside sprint throughput and change lead time, and Scrums.com stays accountable for outcomes, not hours.
Evaluating AI Solutions Architect Talent: Interview Signals, Take-Home Tasks, and Red Flags
The best predictor of architecture skill is a record of systems that ran in production and changed safely afterwards. The methods below expose that record in a few hours.
Interview signals of genuine depth. Ask the candidate to draw the last system they designed, then ask what broke in production and what they changed as a result. Strong architects describe specific failure modes, a trade-off made under constraint, and a decision they would reverse. For AI work, ask which data could reach a model, how they evaluated retrieval quality, and where a human approval step sat in an agent workflow.
Take-home task. Give a two-page brief for a real problem in your estate: a policy admin system that needs an LLM document-extraction service with audit requirements, or accounts that need a landing zone. Ask for a C4 context and container diagram, three decision records with alternatives, and a first-quarter delivery sequence. Time-box it to four hours. Review for clear boundaries, honesty about unknowns, and an early measurable result.
Reference checks. Speak to an engineer who built to the design and an operator who ran it. Ask whether it survived production.
Red flags:
- Every past design is described as a success with no incidents or reversals
- Cannot explain a trade-off without naming a vendor product as the answer
- Treats LLM outputs as deterministic or has no method for evaluation and tracing
- Proposes a full rewrite before an inventory of the current estate exists
- Cannot describe cost attribution for cloud or model usage
How Scrums.com vets. Every architect passes a technical screen on distributed systems, cloud platforms, and AI system design, plus a written design exercise reviewed by a senior architect. AI-assisted engineering certification is part of the standard. To review shortlisted profiles, start a conversation with the team.