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AI talent

The hardest roles to fill did not exist two years ago.

The industry answered AI by buying AI tools for itself. We built a practice that places the people.

72h
Brief to shortlist
Three screened candidates from a complete brief.
2:1
CVs per interview
Candidates sent for every one you interview.
80%
Contracts renewed
Placed contractors extended at least once.
50+
Programmes staffed
Enterprise platform programmes, not placements.

Yallo internal delivery, shortlist and placement records and programme register, as at 30 July 2026.

The gap

These seats stay open because the screen is the hard part.

The market is short of people, and the shortlist is short of evidence. Both have to be solved, and only one of them is a sourcing problem.

72%

of employers cannot find the skills they need, and AI has overtaken engineering and traditional IT as the hardest category of all.

ManpowerGroup Talent Shortage Survey, 2026
  • A practice, not a keyword

    The industry bought AI tools; we built the desk that places people

    AI talent is a named specialism here with its own screening, not a skill listed among professions. That is the difference between a CV that mentions a model and a contractor who has shipped one.

    72%Employers short of skills

  • Screened on evidence

    Read by someone who has shipped an agent

    Screening looks for evaluation discipline, retrieval design and cost control: the things that decide whether an AI build survives contact with production.

    2:1CVs per interview

  • On your existing stack

    Placed onto the platforms you already run

    Azure AI, Databricks and the enterprise platforms around them. Yallo Talent staffs work on the systems you own; building new AI-native systems is saasinator's line, not this one.

  • In region, at pace

    Four entities, three demand markets

    London, Dubai, Riyadh and Bengaluru, so a specialist can start on your paper or ours without an entity of your own.

    72hBrief to shortlist

Yallo internal delivery, shortlist and placement records and programme register, as at 30 July 2026.

Role families

The AI roles we screen, and the mis-hire behind each one.

Every family carries its own screening tests and its own failure mode. They are not variations on one job.

Agentic AI Developer

Builds systems where a model plans, calls tools and acts across steps, rather than answering one prompt at a time.

Prompt and LLM Engineer

Designs, tests and maintains the prompt and context layer, and holds output quality over time.

AI and GenAI Solution Architect

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

MLOps and LLMOps Engineer

Makes AI systems deployable, observable and reversible: pipelines, evaluation in CI, monitoring and cost control.

AI Product Manager

Owns the problem, the adoption and the measurement of value for an AI capability inside a business process.

How we screen

The screen is designed against the failure, not the job title.

  1. Screening is done by someone who has built the thing, not by keyword match against a CV.
  2. Every role has a mis-hire pattern, and the screen is designed to catch that specific one rather than to confirm a general impression.
  3. Evidence of production, not demonstration: what broke, what it cost, and what was rolled back.
  4. Our own delivery work runs on Claude-native systems, so the screen for agentic and prompt roles is built from practice rather than from a specification.

In the estate

AI work lands on the platforms you already run.

Almost none of this is greenfield. The model layer meets an ERP, a CRM or a data estate, and the people who can hold both are the constraint. Five layers, two concerns that cross all of them, the tools at each and the role families we place there. 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.

  • LangSmith
  • Weights and Biases

Also screened against

  • Langfuse
  • Ragas
  • MLflow
  • DeepEval

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

  • LangChain
  • LangGraph
  • CrewAI
  • AutoGen
  • Semantic Kernel
  • Model Context Protocol
  • Pydantic AI
  • n8n

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
  • Hugging Face

Also screened against

  • Meta (Llama)

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
  • Pinecone
  • Elasticsearch
  • Informatica IDMC
  • MuleSoft

Also screened against

  • pgvector
  • Weaviate
  • Qdrant
  • LlamaIndex

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.
Ready to brief us?

Send the role, the platform, the timeline, get a shortlist in 72 hours.

No CVs until we understand your programme. Specialist-screened shortlist matched to your context.

Screened by

  • Packaged Software
  • Architecture
  • Software Development
  • Data & AI
  • Cloud & Infrastructure
  • Agile & DevOps