AI Science Lead - Knowledge Agents

• Sydney, Melbourne, or Brisbane Location
• Hybrid Working

The Opportunity

Westpac is building a reusable Knowledge Agents capability to help frontline employees find accurate, secure and trusted answers when they need them. We are looking for a Lead AI Scientist who can stay close to technical details, set the quality bar and build the team that will deliver this capability at scale.

This is a hands-on leadership role, reporting to the Head of AI. You will own the Consumer knowledge agent technical roadmap, lead complex retrieval, evaluation and agent-design decisions, and work directly with the data, evaluation and delivery questions that determine whether a knowledge agent can be trusted in a regulated banking environment. At the same time, you will recruit, coach and lead a growing team of AI professionals.

Importantly, you will turn fragmented knowledge into one coherent, reusable and well-governed capability that helps frontline teams across contact centres (CCaaS), AI servicing, mortgage operations and branches to serve customers well.

Responsibilities

• Own the Consumer Knowledge Agents technical roadmap with business, Product, Digital and Technology partners, so frontline initiatives build one coherent, reusable capability aligned to divisional and enterprise AI and knowledge management strategies.
• Partner with business and Product owners to frame and prioritise use cases with clear users, benefits, content sources, ownership, risk classification and measurable outcomes.
• Lead hands-on technical direction across knowledge ingestion, retrieval, grounding, orchestration, access controls and workflow integration through agreed architecture and implementation patterns.
• Own end-to-end solution quality, from response evaluation and validation through controlled release and production monitoring, so answers are accurate, grounded, traceable and permission-aware, with effective abstention, escalation and human oversight.
• Ensure every solution meets Group AI risk policies, embedding privacy, security, model risk, Responsible AI, explainability, fairness, human oversight and operational resilience.
• Agree shared components, interfaces, acceptance criteria, operational ownership and integration with frontline workflows across capability and use-case teams.
• Monitor answer quality, adoption, performance and realised value, intervening or escalating where outcomes, controls or timelines are at risk.
• Build reusable components, patterns and quality standards that can be adopted across Consumer and the wider AI Science practice.
• Recruit, lead and develop a high-performing team, creating clarity, psychological safety, rigorous technical review and shared learning.

Essential Skills & Experience

• 8+ years in data science and applied AI, including senior leadership, with recent hands-on practice in complex, highly regulated financial services environments.
• Demonstrated delivery of enterprise knowledge agents, retrieval-augmented generation or related LLM-enabled solutions beyond proof of concept into controlled production use.
• Deep practical expertise across retrieval, grounding, knowledge ingestion, content provenance, agent orchestration, evaluation, monitoring and LLMOps.
• Sound understanding of permission-aware access, privacy, security, model risk, Responsible AI and the controls required for high-trust employee-facing solutions.
• The ability to evaluate answer quality using groundedness, citation accuracy, retrieval quality, latency, cost, source freshness and regression coverage.
• Proven people leadership, including building teams, hiring practitioners, coaching technical talent, setting direction and managing performance and delivery.
• Credibility with senior business and technology stakeholders, with the judgement to explain uncertainty, offer constructive technical review and make practical risk-based decisions.
• Experience with frontline servicing, contact centres, branch operations, mortgage operations or enterprise knowledge management.

Does this sound like you?

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