AVP, AI Engineer, Global Financial Market Technology, Group Technology

Business Function

Group Technology enables and empowers the bank with an efficient, nimble and resilient infrastructure through a strategic focus on productivity, quality & control, technology, people capability and innovation. In Group Technology, we manage the majority of the Bank's operational processes and inspire to delight our business partners through our multiple banking delivery channels.

Responsibilities

• Design and develop Agentic AI, Generative AI, and RAG solutions, including AI agents, orchestration, tool calling, prompt engineering, memory, reflection, and evaluation.
• Build ML and Data Science models for client propensity, recommendations, classification, ranking, behavioral analytics, and next-best-action use cases.
• Analyze structured and unstructured data, performing data exploration, feature engineering, model evaluation, and performance monitoring.
• Develop scalable, production-ready AI services and APIs and integrate them with enterprise systems and data sources.
• Build reusable AI components and frameworks that can support multiple business use cases.
• Implement monitoring, observability, and evaluation capabilities while optimizing AI solutions for scalability, performance, accuracy, and cost.
• Partner with business stakeholders, product owners, data scientists, and engineers to translate Global Financial Markets requirements into AI-powered solutions.

Requirements

• Hands-on experience in AI/ML Engineering, Data Science, or Advanced Analytics, including deploying ML/AI solutions into production.
• Strong proficiency in Python, SQL, and REST APIs.
• Experience with ML frameworks such as Scikit-Learn, XGBoost/LightGBM, TensorFlow, or PyTorch.
• Mandatory Generative AI experience covering RAG, Vector Search, LLM applications, Dify, and Agentic AI frameworks.
• Knowledge of data pipelines, data transformation, feature engineering, and data quality management.
• Experience with OpenShift (OCP), Docker, Kubernetes, CI/CD, and Git.
• Preferred experience in banking, financial services, capital markets, or wealth management.
• Advantageous experience in recommendation/personalization engines, search and retrieval, knowledge management, MLOps/LLMOps, AI governance, and enterprise document repositories.

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