​Program Scope:

Developing and provisioning of infrastructure that support agentic workflows across both Azure and GCP

Data science expertise to help shape the agentic solution design of Coach AI as well as target state AI Assistant

Creation of agent integration patterns to fulfil and action on behalf of customers by leveraging Bank systems

Development of new AI products for the Conversational Banking Lab, including but not limited to:

Agent summarisation - bespoke features to summarise nuanced conversations

App search evolution - transforming the existing vector search functionality into a fully generative experience and transition to single experience point

Evaluation methods - continued automation of both deterministic and generative conversations in a scaled and simulated way

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ML & AI Engineer

Must haves

1. Python literacy - min 2years+ working on production grade LLM based applications

2. Software Engineering - strong understanding of microservices architecture, CI/CD pipelines, event driven architecture

3. AI Engineering - RAG pipelines, prompt engineering, VertexAI experience, LLMOps and runtime evaluation/monitoring

4. Data engineering - scaled data pipelines using python/spark. GCP native services: BQ, Spanner, Dataflow, Firestore

Nice to haves

1. AgenticAI - Langgraph, ADK, CrewAI, multi-agent architectures - experience in building deployable solutions, not jupyter notebooks

2. Data ontologies - graphical approaches to data storage + retrieval

• Exposure to Agile/Scrum methodologies.