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 & ExperienceMust 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.