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
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
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.
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
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
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.
#J-18808-Ljbffr…