Staff AI Engineer

Staff AI / GenAI Engineer – Platform Engineering

Our client, a leading global banking organisation, is building a next-generation AI Platform Engineering function responsible for delivering scalable AI capabilities across the business.

This is a rare opportunity to join a high-calibre engineering team at the early stages of a major enterprise AI transformation. The team is building cutting-edge GenAI platforms, automation agents, and reusable AI services that will underpin AI adoption across the organisation.

Working within a highly innovative, greenfield environment, you will help shape the architecture, tooling, and engineering standards behind enterprise-scale AI systems in a business-critical setting.

The successful candidate will be a highly hands-on engineer with strong experience building production-grade AI products and scalable cloud-based systems.

Key Responsibilities

• Build scalable AI/GenAI platform services and infrastructure
• Develop AI-powered applications, automation agents, and enterprise AI workflows
• Build RAG pipelines, vector search capabilities, and orchestration frameworks
• Develop backend services and APIs using Python
• Implement LLMOps/MLOps best practices including monitoring, evaluation, observability, and guardrails
• Design scalable solutions capable of supporting enterprise-wide adoption
• Contribute to architecture, technical standards, and platform engineering best practices

Required Experience

• Strong hands-on Python engineering experience
• Experience building and deploying AI/GenAI products in production environments
• Strong knowledge of Azure and Databricks
• Experience with RAG architectures, embeddings, vector databases, and prompt engineering
• Experience with AI orchestration frameworks such as LangGraph, LlamaIndex, or similar
• Strong backend engineering and cloud infrastructure experience
• Experience building scalable enterprise-grade systems
• Comfortable operating in fast-moving, greenfield environments

Desirable Experience

• AKS / Kubernetes
• MLflow / LLMOps tooling
• AI observability and evaluation tooling
• Customer-facing AI products or chatbots
• Financial services or regulated environment experience