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AI talent

AI and GenAI Solution Architect

Owns the target architecture: which model, hosted where, grounded on what, governed how, and integrated into which enterprise estate.

The role

What the job actually is.

The architect makes the decisions that are expensive to reverse. Model and hosting choice, where data sits and under whose jurisdiction, how the system is grounded, and how it reaches the platforms the business already runs. On an enterprise programme the last of those is the hard part, because the AI layer is rarely the constraint and the estate almost always is. This role has to be fluent in both, and has to survive a security review and a cost conversation on the same design.

What we screen for

The questions that separate the field.

Asked by someone who has built the thing, and designed to catch this role's specific failure rather than to confirm a general impression.

  1. Can they defend a build-versus-buy call in front of a CIO, including the case they rejected.
  2. Do they design for latency, cost per interaction and data residency, or only for capability.
  3. Do they know the client's platform estate rather than only the model layer. Ask what the integration actually lands on.
  4. What they have taken out of a design after security review, and what it cost.

The common mis-hire

The one you have probably already made.

A cloud architect with a certification and no shipped AI system, or a data scientist who has never had to satisfy a security review. Both produce a design that is coherent on a slide and stalls at the first governance gate.

In the estate

Where this role works, and what we screen it against.

The layers this family works at, lit. These are the tools we screen against. Naming one says we can test for it, not that we have delivered on it.

Evaluation and observability

Spans every layer. Without it a system is shipped on impressions.

Experience and delivery

Copilots and agents inside a business process, and the interaction design that makes an uncertain system usable.

  • Salesforce Agentforce
  • SAP AI Core and Joule
  • Oracle AI Services
  • Power Automate
  • UiPath

Orchestration and agents

Where an agent's steps, tools and state are defined, and where its failures are caught before a user meets them.

Also screened against

  • LangGraph
  • Semantic Kernel
  • Model Context Protocol
Role families we place here

Models

The models themselves, and the platforms an enterprise hosts them through.

  • Anthropic (Claude)
  • OpenAI
  • Google (Gemini)
  • Mistral
  • Cohere
  • Azure AI Foundry
  • AWS Bedrock
  • Google Vertex AI
  • AWS SageMaker

Also screened against

  • Meta (Llama)
Role families we place here

Data and grounding

What the model is grounded in, and the integration work that gets enterprise data to where it can reach it.

  • Databricks Mosaic AI
  • Snowflake Cortex
  • Microsoft Fabric
  • Azure AI Search
  • Elasticsearch
  • Informatica IDMC
  • MuleSoft

Also screened against

  • Weaviate
  • Qdrant
Role families we place here

Systems you already run

The seven platform desks Yallo staffs. Almost no AI work is greenfield; it lands here.

Role families we place here

Governance, risk and safety

Spans every layer. Named as what governance roles are screened against; what any of them obliges is your counsel's call.

  • EU AI Act
  • ISO/IEC 42001
  • ISO/IEC 23894
  • NIST AI Risk Management Framework
  • OWASP Top 10 for LLM Applications
Role families we place here
Naming a technology here says we screen against it, not that we have delivered on it. The role families on each layer are the ones we place there.

Seniority

What changes between mid, senior and lead.

The grade is a description of what the person owns, not a band. Rates come with the shortlist.

Mid
Designs a component against an agreed target architecture. Not yet the person defending the estate-level call.
Senior
Owns the target architecture for a capability end to end, including hosting, residency and the integration into the estate.
Lead
Holds the architecture across a portfolio, sets the reference patterns, and is accountable for the decisions that are expensive to reverse.

In a programme

When this role is needed, and what blocks it.

First in and last out. Present from discovery, because the hosting and residency decisions gate everything downstream, and retained through deployment because the design is tested by integration rather than by build. The common sequencing error is appointing this role after the model has already been chosen, which turns architecture into justification.

Ask

Send the brief, get a screened AI Solution Architect shortlist.

Tell us the programme, the stack and the timeline.