AI Transformation Group Manager

The AI Transformation Group Manager is a senior management-level position responsible for leading and transforming the technology organization that builds and operates Master and Reference Data capabilities across Citi’s Institutional Client Group. Leading an organization of approximately 70 engineers, engineering managers, and delivery leads, this role owns the end-to-end modernization of how the group designs, builds, tests, ships, and operates software — re-architecting the software development lifecycle around applied artificial intelligence. The overall objective of this role is to make the organization measurably AI-first in its mindset, delivery methods, and work products, while raising the quality, timeliness, and resilience of the legal-entity, counterparty, instrument, and security reference data on which the firm’s trading, risk, and client franchises depend.

Responsibilities:

• Set and own the AI transformation strategy and multi-year roadmap for the Master and Reference Data organization, translating frontier AI capabilities into prioritized, measurable engineering and business outcomes across the full software development lifecycle — from requirements and design through coding, testing, release, and production operations.
• Lead, motivate, and develop an organization of approximately 70 technologists, including hands-on engineering managers and senior individual contributors; own performance evaluation and management, talent selection, capability building, compensation, succession, and resource planning; and reshape the operating model to sustain an AI-first way of working.
• Drive the organization’s transition to AI-augmented engineering — embedding AI coding assistants, agentic development workflows, and automated test and review generation into daily delivery; redesigning workflows and quality gates so that accelerated upstream output does not create downstream bottlenecks; and establishing the standards, guardrails, and evaluation criteria that let teams move quickly and safely.
• Direct the design, build, and operation of Master and Reference Data platforms and services — golden-record mastering, entity resolution and disambiguation, hierarchies and cross-referencing, data-quality remediation, lineage, and distribution — primarily on a Scala-based engineering stack (e.g., Scala and Akka for large-scale data processing), adopting Python and other languages where they are the right tool for the problem.
• Lead the application of AI to the data domain itself: design and oversee solutions that consume large language models and AI services (via APIs and internal AI platforms) for entity matching, anomaly and break detection, classification and enrichment, natural-language data discovery and stewardship, and retrieval-augmented access to data catalogs and documentation — with rigorous evaluation for accuracy, hallucination, lineage, and domain-constraint validation.
• Own delivery accountability end to end: run a high-performing engineering pipeline to demanding code, test, and operational standards, and instrument the organization with metrics for delivery velocity, quality, reliability, AI adoption, and realized business value (ROI), continuously refining the approach against those outcomes.
• Champion AI fluency across the organization as a practice pusher — establishing internal standards, reusable frameworks, reference architectures, and best practices; advising teams on tooling and technique selection; and building the upskilling paths that turn engineers into effective orchestrators, reviewers, and validators of AI-generated work.
• Partner with product, architecture, data governance, and consuming business and technology teams across the Institutional Client Group to align the AI-first roadmap with enterprise architecture and controls and with the needs of downstream consumers, delivering reference data as scalable, secure, well-governed services.
• Resolve complex, ambiguous problems whose impact extends beyond the immediate organization, applying deep technical judgment and drawing on a diverse range of internal and external sources to make sound build/buy/partner and architecture decisions.
• Persuade and influence senior stakeholders, vendors, and platform partners through clear communication, diplomacy, and negotiation, translating technical strategy into outcomes that non-technical executives can act on.
• Appropriately assess risk when business decisions are made, demonst…