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

Remote
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