Principal AI Architect, London Stock Exchange

• Strong experience with supervised, unsupervised, and generative models
• Excellent understanding of model lifecycle, from experimentation to production
• Proven hands-on experience with key AI technologies and concepts such as LLMs, Scikit, prompt engineering, LangChain, vector databases, graph databases, Lucence, Lakehouse and retrieval-augmented generation (RAG)
• Strong grasp of software engineering principles (SOLID, design patterns), dependency injection, and scalable system design
• Hands on experience with ML tooling such as MLflow, Snowflake, Databricks, SageMaker or Azure Machine Learning
• Hands on production experience deploying and monitoring AI/ML systems on cloud platforms such as AWS, GCP or Azure

Desirable

• Reporting to the Director of Architecture within the Risk Intelligence team
• Risk Intelligence is responsible for developing critical services that support anti-money laundering efforts in the financial markets
• We’re seeking a forward-looking Principal AI Architect to lead and oversee the design, development, and governance of our AI and ML platform
• This is a hands-on, strategic role where you’ll help to shape the future of our AI capabilities, coach engineers, and ensure our tooling and frameworks are cutting-edge
• You’ll be instrumental in driving innovation across both machine learning and emerging generative AI technologies
• Architecture Leadership: Define and evolve the architecture for AI/ML systems, ensuring scalability, governance, data structures and paradigms (Lakehouse, warehouse, etc), data lineage, shared runtime, common AI workspaces and performance
• Code & Model Reviews: Conduct technical reviews of both code and AI/ML models, ensuring quality, reproducibility and adherence to groups and industry best practices
• Tooling & Developer Experience: Own and enhance the AI/ML tooling ecosystem including experiment tracking, data pipelines, data access, model deployment pipelines, CI/CD for ML, and observability
• Framework Contribution: Contribute to shared libraries and frameworks that support model training, evaluation, and deployment
• Innovation & Research: Stay ahead of developments in GenAI (e.g. LLMs, multimodal models), ML frameworks (e.g. PyTorch, TensorFlow, Scikit-learn), orchestration tools (e.g. LangChain, LangFuse) and stay connected with AI community e.g. Hugging Face
• Engineering Excellence: Apply SOLID principles, design patterns, and dependency injection to AI systems, ensuring maintainability and robustness
• Data & Model Governance: Ensure responsible AI practices, including model explainability, fairness, and compliance with data governance standards
• Cross-Functional Collaboration: Partner with product and platform teams to deliver intelligent, scalable solutions that solve real-world problems
• Mentorship & Coaching: Guide AI engineers and data scientists, fostering a culture of technical excellence, experimentation, and continuous learning