Generative AI Engineer

Founding AI Engineer - Build an AI Capability From the Ground Up

A leading global investment business is looking to make its first dedicated AI Engineering hire - a rare opportunity to build and shape AI capability from day one inside a highly respected organisation managing tens of billions in assets.

This is far beyond experimentation or internal demos. The successful candidate will design, build, and own production-grade AI systems that solve real business problems - from advanced RAG pipelines and agentic workflows to internal AI platforms used across the organisation.

This role offers genuine autonomy, visibility, and the chance to influence AI strategy, architecture, and engineering standards at an early stage.

Why this opportunity stands out

• Foundational AI hire with significant ownership and influence
• Greenfield environment with freedom to shape tooling, architecture, and best practices
• Direct exposure to complex, high-value datasets and workflows
• Strong long-term investment and commitment to AI from leadership
• Opportunity to build systems with immediate, measurable impact

The role

• Build production-grade RAG pipelines across complex unstructured data
• Design and deploy multi-agent AI systems and orchestration frameworks
• Integrate LLMs across OpenAI, Anthropic, and open-source ecosystems
• Develop semantic search, vector database, and graph-based retrieval systems
• Own AI evaluations, observability, governance, and reliability
• Build internal AI products that enhance decision-making and operational efficiency

• Proven experience shipping production AI/LLM systems used by real users
• Strong end-to-end engineering capability - from architecture through deployment
• Deep Python and backend engineering experience
• Strong understanding of modern AI tooling, RAG, and agentic systems
• Comfortable operating in ambiguity and building from scratch
• Uses AI tooling aggressively, but critically and responsibly
• Cares about product quality, reliability, and business outcomes - not just models

Tech environment