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AI Architect

AI Architect

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Overview

My client is accelerating the adoption of applied AI systems across a highly performance-driven, decision-critical environment. This role sits within a central AI Engineering function and is embedded alongside senior decision-makers, helping accelerate the identification, delivery, and adoption of AI-powered systems.

The focus is on translation, acceleration, and adoption – shaping high-value ideas with domain experts, and working closely with AI Engineering and Software Engineering teams to productionise solutions in live, high-stakes environments.

A core part of the role is increasing AI fluency across the organisation, helping expert users understand what is already possible, how existing capabilities can be applied, and where newer approaches (including agentic workflows, LLMs, GenAI, RAG, MCP, MLOps) can deliver real advantage.

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Requirements

Significant experience delivering production software or AI-enabled systems

Strong understanding of modern AI approaches, including LLMs, retrieval, and agentic workflows

Experience collaborating closely with AI Engineers on system design and production readiness

Experience owning technical direction while working with strong engineering teams to deliver at scale

Comfortable operating between technical teams and expert non-technical users

Proven ability to drive adoption of complex systems in high-stakes environments

Clear, pragmatic communicator with strong technical judgement

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If you would like to discuss this in more detail, please send your application to James@atlanticsearch.com to arrange a call. Thanks!

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Responsibilities

    Embed with senior stakeholders to understand workflows, judgment points, and constraints

    Identify opportunities where applied AI can improve insight, speed, or automation

    Translate ambiguous ideas into clear, actionable technical initiatives

    Own solution design and partner with AI Engineering and Software Engineering teams to deliver and productionise AI systems

    Accelerate adoption of existing AI systems by reducing friction and time-to-value

    Act as a trusted AI partner, shaping expectations and effective usage

    Capture successful approaches and feed them back into shared AI standards and tooling

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