Responsibilities
- You'll take ownership of the architecture for enterprise AI capabilities, including:
- Agentic AI platforms, AI assistants, copilots and autonomous workflows
- Multi-agent systems incorporating reasoning, planning, memory, orchestration and tool integration
- Production-grade LLM and RAG architectures
- Intelligent search, knowledge assistants, workflow automation and decision-support systems
- Enterprise LLMOps, MLOps, observability and AI governance
- AI security, guardrails, evaluation and Responsible AI
- Cloud AI platforms and hybrid/multi-cloud architecture
You'll also provide technical leadership across architects, engineers and data scientists while working with senior stakeholders to shape AI strategy, roadmaps, investment priorities and transformation programmes.
Experience Required
- We're looking for an established AI architecture or engineering leader with:
- 15+ years of software engineering, architecture or platform leadership
- 8+ years delivering AI/ML platforms into production
- Proven experience architecting and deploying Agentic AI, LLM and Generative AI solutions at enterprise scale
- Strong experience with RAG and LLM application architectures
- A background within banking, financial services or another highly regulated environment
- Experience leading large engineering teams and complex technology transformations
- A track record of taking AI platforms from strategy and architecture through to production and operationalisation
- Strong executive stakeholder engagement and the ability to translate AI capabilities into measurable business outcomes
Technology
Agentic AI & GenAI
Multi-Agent Systems | Agentic AI Architecture | RAG | LLM Applications | Prompt Engineering | Responsible AI | AI Evaluation
Frameworks: LangChain | LangGraph | Semantic Kernel | AutoGen
Cloud & AI Platforms
Azure AI Foundry | Azure OpenAI | AWS Bedrock | SageMaker | Google Vertex AI | Hybrid/Multi-Cloud
Engineering & Platform
Python | Java | Scala | TypeScript | SQL | Kubernetes | Docker | Terraform | Bicep | CI/CD | Infrastructure as Code
AI Governance & Observability
LLMOps | MLOps | Model Governance | AI Security & Guardrails | LangSmith | Arize | Monitoring & Evaluation Frameworks
Qualifications
- Relevant qualifications may include:
- Master's degree in Computer Science, AI, Data Science or related discipline
- AWS Solutions Architect Professional
- AWS AI/ML Specialty
- Microsoft Azure AI Engineer
- Google Professional ML Engineer
- Executive AI / Machine Learning qualifications
If you're an AI Architect, Lead AI Architect, Head of AI Engineering, AI Platform Lead, Director of AI Engineering or senior AI technology leader with genuine production-scale experience, please feel free to submit your CV.