Lead AI Engineer – LLM & Recommendation Systems

Our client, a well-established Financial Services organisation, is building a next-generation AI-powered content platform focused on trading and financial intelligence. They are looking to appoint a Lead AI Engineer to play a key role in designing, building, and scaling production-grade AI systems in a highly regulated environment.

This is a senior, hands-on leadership role, working at the intersection of open-source AI, large language models (LLMs), fine-tuning pipelines, and personalised recommendation systems, supporting large-scale user engagement and real-time decisioning.

Key Responsibilities

• Lead the design, development, and optimisation of advanced AI models, including LLMs and multimodal models, to deliver personalised trading and financial content
• Architect and operate end-to-end AI platforms, covering data ingestion, model training, evaluation, fine-tuning, deployment, and production monitoring
• Own and evolve custom fine-tuning pipelines using open-source frameworks, ensuring alignment with regulatory, security, and data governance requirements (e.g. KYC, AML)
• Design and implement personalised recommendation and search systems, leveraging techniques such as graph-based models, user embeddings, and behavioural intelligence
• Build and manage real-time feature pipelines using large-scale user interaction, market, and behavioural data
• Implement robust feedback loops and experimentation frameworks to continuously improve model relevance, accuracy, and performance
• Partner closely with product, data, engineering, and compliance stakeholders to translate business requirements into scalable AI solutions
• Provide technical leadership and mentorship to AI and data engineers, setting best practices across model development, deployment, and operations
• Contribute to architectural decisions around AI governance, scalability, reliability, and cost optimisation

Required Experience & Qualifications

• At least 10 years of experience in AI / Machine Learning / Data Engineering, with significant exposure to production-grade systems
• Strong hands-on experience working with open-source AI frameworks, including PyTorch, Hugging Face, and related ecosystems
• Proven track record of building, deploying, and operating LLM-based systems in real-world production environments
• Deep experience with model fine-tuning, evaluation, and optimisation, especially using large-scale user behaviour data
• Solid background in feature engineering, data pipelines, and real-time systems
• Experience with model serving and inference platforms such as Triton, TorchServe, BentoML, or similar
• Bachelor’s degree or higher in Computer Science, Engineering, or a related discipline