AI Solutions Engineer: £138,000 + Bonus (Finance)

Hunter Bond; Greater London; Hybrid; GBP 82,000 - 138,000; Full time

Job summary

Hunter Bond is seeking an AI Solutions Architect in London to own the end-to-end design of AI solutions, from use-case intake to production delivery. The role sits in a global banking group’s AI & Innovation team within Architecture & Engineering, offering hybrid work in a regulated enterprise environment.

You will translate business requirements into architectures, design across Copilot, Azure AI, Power Platform, and GCP, and drive governance through SDA and other forums.

Qualifications

• Experience delivering AI solutions in regulated enterprises.
• Ability to translate requirements into end-to-end architectures.
• Hands-on delivery with production readiness and governance.
• Experience with enterprise AI platforms.

Responsibilities

• Own AI solutions from intake through to production deployment.
• Translate business requirements into end-to-end solution architectures aligned to enterprise technology stack.
• Lead solution design through SDA and internal architecture governance forums.
• Own architecture artefacts — design documents, technical specs, integration patterns, governance submissions.
• Drive AI governance processes — risk assessments, control alignment, production readiness.

Skills

GenAI architectures

LLMs

Prompt engineering

Solution design

Governance & security

Enterprise AI integration

Tools

Microsoft 365 Copilot

Copilot Studio

Azure AI

Power Platform

GCP

SharePoint / Graph

Job description

AI Solutions Architect | London (Hybrid) | Global Banking Group

Salary: Up to £138,000 + Bonus

A leading global banking and financial services group is expanding its AI & Innovation team within Architecture & Engineering, at a pivotal stage of its enterprise AI journey. We're looking for a hands-on AI Solutions Architect to take end-to-end ownership of AI solution design — from use case intake through to governance approval and production delivery.

The Role

• Own AI solutions from initial use case intake through to production deployment
• Translate business requirements into end-to-end solution architectures aligned to enterprise technology stack
• Design and implement AI solutions across Microsoft 365 Copilot, Copilot Studio, Power Platform, Azure AI services, and GCP
• Lead solution design through Solution Design Authority (SDA) and internal architecture governance forums
• Own architecture artefacts — design documents, technical specs, integration patterns, governance submissions
• Drive AI governance processes — risk assessments, control alignment, production readinessAct as technical point of accountability for solution design decisions across the delivery lifecycle
• Design with full consideration of integration, data flows, identity, security boundaries, and operational resilience
• Support prototyping, validation, and troubleshooting hands‑on alongside engineering teams
• Develop reusable architecture patterns and technical guidance for common AI use cases

What You’ll Bring

• Strong understanding of GenAI solution architectures — LLMs, agent‑based patterns, workflow orchestration, enterprise AI integration
• Practical experience with RAG, knowledge grounding, prompt engineering, human‑in‑the‑loop design
• Experience designing secure, governed AI solutions — guardrails, responsible AI, explainability
• Proven delivery of complex technology solutions in a regulated enterprise environment, ownership from requirements through production
• Hands‑on solution architecture experience — translating business needs into implementable designs
• Experience with enterprise AI platforms (Microsoft 365 Copilot, Copilot Studio, Azure AI, Power Platform, or equivalent)
• Ability to lead through formal architecture governance processes (SDA or equivalent)
• Experience with AI governance, risk, and control functions — security, data privacy, auditability, regulatory compliance
• Strong technical foundation in enterprise architecture — cloud, infrastructure, networking, IAM, security
• AI delivery within financial services or another highly regulated industry
• Familiarity with AI governance frameworks — EU AI Act, GDPR, DORA
• Azure AI/Azure OpenAI experience with RAG, grounding, prompt orchestration in practice
• Enterprise data/knowledge architecture — semantic search, vector databases, embeddings
• Exposure to Microsoft Graph, SharePoint, Teams/Outlook integration
• Emerging Microsoft AI ecosystem exposure — Microsoft Foundry, Fabric, or equivalent