Ai Architect

We are growing our consulting team, and looking for an experienced AI Architect to lead the design and delivery of secure, scalable and responsible AI solutions across enterprise and public‑sector programmes. This role combines hands‑on technical expertise with strong architectural leadership, shaping end‑to‑end AI solutions from early discovery through to production delivery and scale.

You will act as a trusted advisor to clients and internal teams, defining AI architectures, setting standards and guardrails, and ensuring AI is applied where it delivers real business value.

• Design and own end‑to‑end AI client‑specific solution architectures, including Generative AI, Agents and traditional ML.
• Define and maintain enterprise AI architecture patterns, reference architectures, and reusable building blocks within the Microsoft ecosystem.
• Lead AI governance, security and Responsible AI by design, ensuring solutions meet regulatory, ethical and organisational standards.
• Provide architectural oversight across delivery teams, ensuring quality and scalability.
• Support pre‑sales and discovery, shaping solution approaches and technical estimates and delivery roadmaps.
• Advise stakeholders on AI feasibility, trade‑offs, risks and long‑term sustainability.
• Build hands‑on prototypes, reference implementations and reusable accelerators to remain close enough to the code to set credible technical direction and unblock delivery teams.

• Significant overall experience delivering AI and Data solutions in complex enterprise environments, with a minimum of 5 years hands‑on AI delivery experience.
• Proven experience delivering production‑grade AI solutions end‑to‑end, from discovery through to live operations and ongoing model management.
• Hands‑on expertise across Microsoft cloud and AI ecosystem, including Microsoft Foundry and data platform.
• Strong experience with integrating low‑code Power Platform solutions into enterprise‑scale solutions, including hands‑on experience with Copilot Studio.
• Solid understanding of Microsoft security, identity and compliance (Entra , RBAC, network isolation, data protection).
• Strong experience designing LLM‑based solutions, including Retrieval‑Augmented Generation (RAG), prompt engineering strategies and agent‑based architectures, leveraging frameworks such as Semantic Kernel, Lang Chain, and Microsoft Agent Framework.
• Deep practical experience with classical Machine Learning, including supervised and unsupervised learning, feature engineering, model selection, training pipelines, evaluation frameworks and production lifecycle management, equivalent in depth to GenAI expertise.
• Proven ability to assess client use cases and make well‑reasoned architectural decisions between classical ML and Generative AI approaches; and articulate clearly why.
• Strong experience in cloud‑native architecture, including API‑based and event‑driven designs, enterprise integration patterns and architectures focused on scalability, resilience and reliability.
• Experience with Dev Ops, MLOps and LLMOps practices, including CI/CD pipelines, automated testing, environment strategies and operating LLM‑based solutions in production, with a strong focus on model and prompt lifecycle management, observability and evaluation, reliability and cost management.
• Strong understanding of Generative AI governance, operational controls and cost evaluation, including monitoring usage, performance and designing solutions that are supportable, auditable and sustainable at scale.
• Strong experience embedding Responsible AI and governance into Generative AI solutions, covering safety, risk management, transparency, explainability, human‑in‑the‑loop design and…