Our client, a Financial Services organisation, is seeking an experienced AI Engineer to join a growing AI and data engineering function. This role focuses on building and operating production-grade AI systems tailored for financial use cases, leveraging large-scale data from multiple financial and media sources.

The successful candidate will work on advanced AI engineering initiatives, including LLMs, multimodal models, and recommendation systems, within a regulated and security-sensitive environment.

Key Responsibilities

• Design, fine-tune, and optimise AI models, including Large Language Models (LLMs) and multimodal models, to deliver personalised, finance-related content
• Build and operate end-to-end AI platforms covering data ingestion, model training, evaluation, optimisation, deployment, and production operations
• Develop and maintain custom fine-tuning pipelines aligned with financial regulatory and security requirements (e.g. KYC, AML, data governance)
• Design and implement personalised recommendation systems using advanced techniques such as graph-based recommendation models, informed by user behaviour patterns
• Build real-time feature pipelines using large-scale user interaction and behavioural data
• Implement feedback loops to continuously improve model accuracy, relevance, and performance in production

Required Qualifications

• 3+ years of experience as an AI / Machine Learning Engineer in a production environment
• Bachelor’s degree or higher in Computer Science, Engineering, or a related discipline
• Strong hands-on experience with modern deep learning frameworks such as PyTorch and Hugging Face
• Proven experience developing, deploying, and operating deep learning models at scale using user behaviour data
• Strong experience working with LLMs or multimodal models in production settings
• Solid background in feature engineering and data pipeline development
• Experience with model serving and inference platforms such as Triton, TorchServe, or BentoML

Preferred Qualifications

• Prior experience working in Financial Services, fintech, or other regulated, data-intensive environments
• Experience designing or operating LLMOps / MLOps platforms
• Experience with GPU profiling, performance optimisation, and tuning
• Exposure to training and deploying AI models in hybrid cloud or multi-environment architectures
• Technical leadership experience or ownership of AI-driven initiatives
• Hands-on experience with Kubernetes and Docker for deployment and operations
• Academic publications in AI/ML or contributions to open-source projects