We are currently looking for an experienced ML & AI Engineer to join a major technology program delivering advanced AI-driven solutions within the banking sector. The role involves working on innovative AI initiatives, building scalable infrastructure, and developing intelligent systems that power agent-based workflows and conversational AI platforms. You will collaborate with cross-functional teams to design and implement next-generation AI capabilities and help drive the evolution of AI-powered products. Program Scope Develop and provision infrastructure that supports agentic AI workflows across both Azure and Google Cloud Platform (GCP) environments. Provide data science expertise to support the design of agent-based solutions, including Coach AI and future AI Assistant capabilities. Create integration patterns for AI agents to interact with banking systems and perform actions on behalf of customers. Contribute to the development of new AI products within the Conversational Banking Lab. Key Initiatives Include Agent Summarisation Develop advanced capabilities to summarise complex and nuanced customer conversations. App Search Evolution Transform existing vector search functionality into a fully generative AI-driven search experience, creating a single unified interface for users. Evaluation Methods Build automated evaluation frameworks to test and validate both deterministic and generative AI conversations at scale. Required Skills & Experience Must Have Strong Python development skills, with 2 years of experience building production-grade applications using Large Language Models (LLMs). Solid understanding of software engineering principles, including: Microservices architecture CI/CD pipelines Event-driven architecture Hands-on experience with AI engineering practices, including: RAG (Retrieval-Augmented Generation) pipelines Prompt engineering LLMOps Runtime monitoring and evaluation of AI systems Experience with Vertex AI Experience in data engineering, including building scalable data pipelines using Python and Spark. Strong knowledge of GCP-native services, including: BigQuery (BQ) Spanner Dataflow Firestore Nice to Have Experience with Agentic AI frameworks, such as: LangGraph ADK CrewAI Multi-agent architectures Experience building deployable AI solutions (production environments rather than notebook-only solutions). Knowledge of data ontologies and graph-based data models. Exposure to Agile or Scrum development methodologies .