Flagship Programme: Agentic Virtual Accountant (AVA)

• Architected and built the skill layer that makes agentic AI scale in Finance: Finance SMEs author agent skills themselves, rather than engineers hand-writing deterministic Python functions for every process variant.
• Wrote the architecture decision records behind that design. Those ADRs are in use now and govern how the wider portfolio of Finance AI applications is scaled and reviewed, not only AVA.
• Lead AVA itself, spanning every business unit finance team across the bank, targeting the manual touchpoints that run finance operations: month-end close, organisational restructure and redundancy processing. A multi-year programme sequenced with the wider Finance transformation.
• Drive the enterprise definition layer, harmonising metric, process and control definitions across BU finance teams so agents operate against one agreed set of meanings rather than local variants.
• Own data requirements and transformation design end to end, with human-in-the-loop control points, auditability and lineage designed in from the start.

Delivery, Adoption & Product Ownership

• Delivered and change-managed the Finance AI portfolio into business-as-usual, taking products built as Chapter Lead through rollout, training, adoption and operational ownership. SQL Weaver received a CFO quarterly excellence award in 2026.
• Product owner for SQL Weaver as a Service, extending it from a standalone application in production with 2,000 users into a consumable service, delivered with a fully offshore engineering team.
• Run delivery across timezones with squad members in Sydney and Bangalore, setting the operating rhythms and handover practices that make distributed work reliable.
• Partner with the Engineering chapter lead to keep applications lean and available, holding non-functional standards on cost, performance, resilience and uptime alongside feature delivery.
• Take products through the bank's architecture governance: high-level solution architecture (HLSA) reviews and risk assessment forums, defending design decisions to enterprise architects and risk partners.

Management & Leadership

• Two chapters report to me, Reporting & Automation and AI & Platforms, including chapter leads and senior managers. Data scientists are chaptered into my squads, where I lead them on delivery.
• Provide system-level stewardship to build scalable analytics/engineering capability across domains and deliver long-term business outcomes.
• Strengthen leadership across chapters by improving prioritisation discipline, delivery standards and capability benches for current demand and future strategy.
• Translate innovation into sustainable BAU by establishing clear ownership, operating rhythms, monitoring and lifecycle management from day one.
• Align delivery to governance, risk and control expectations; make trade-offs explicit and ensure accountability is clear when demand exceeds capacity.
• Anchor investment and sequencing decisions to measurable business impact, resilience and maintainability at scale.

Technical & Engineering

• Build strong engineering foundations through disciplined data engineering, governed pipelines and robust feature engineering practices.
• Ensure AI/ML techniques are embedded deliberately and responsibly within production environments (not as isolated initiatives).
• Drive production-grade design standards for reliability, observability, security and operability across data/ML systems.
• Establish patterns for reusable data products and fit-for-purpose platforms that improve productivity and decision-making in measurable ways.