Lead AI Engineer

Looking for a Lead AI/Data Engineer to play a critical role in advancing the enterprise AI and data platform. This individual will help scale an in-flight AI and analytics initiative that supports investment decision-making, knowledge management, and data-driven insights across the business.

This is a hands‑on technical leadership role for someone who enjoys building, architecting, and deploying production‑grade AI and data solutions in a cloud‑first environment.

• Design and implement scalable AI and data platforms that unify structured and unstructured internal and external data to support advanced analytics and LLM‑powered workflows.
• Build and optimise Retrieval‑Augmented Generation (RAG) pipelines, including embedding strategies, vector search, and retrieval performance tuning.
• Develop and maintain robust data ingestion and transformation pipelines across multiple data sources.
• Architect backend services and APIs in Python to expose AI and analytics capabilities such as semantic search, summarisation, and decision‑support tools.
• Collaborate closely with business and technology stakeholders to translate real‑world use cases into scalable solutions.
• Lead technical direction, mentor engineers, and establish best practices across AI, data, and platform engineering.
• Ensure reliability, observability, and governance of AI systems, including monitoring, evaluation frameworks, and security standards.
• Drive the transition from proof‑of‑concept to enterprise production environments.

• 7+ years of experience across AI engineering, data engineering, backend development, or applied machine learning.
• Strong hands‑on Python development experience, including API design and scalable system architecture.
• Proven experience designing and deploying production AI or data platforms in cloud environments.
• Deep experience with Azure‑based data and AI ecosystems (e.g., Azure OpenAI, Cosmos DB, Azure ML, or related services).
• Experience building RAG systems, vector search workflows, or modern LLM applications.
• Strong understanding of data architecture, pipelines, and distributed systems.
• Ability to balance technical depth with strategic thinking and cross‑functional collaboration.

• Background working with financial, investment, or real asset data.
• Experience in regulated or enterprise environments.
• Exposure to graph data, knowledge modelling, or advanced analytics.

• Opportunity to shape a high‑impact AI platform with real business visibility.
• Hands‑on architecture and ownership in a rapidly evolving area of technology.
• Collaborative environment with strong leadership support and long‑term investment in AI and data capabilities.