Head of AI Solutions, COO Technology - MD (C16) (New York)

Job ID: 98062867120Posted: 2026-07-24Location: New York, New York, United StatesSalary: $250,000.00 - $500,000.00Category: Technology, Applications Development, ExecutiveCompany: CitiCiti's COO Technology organization is building one of the most consequential AI platforms in global financial services — and this is the role that leads it.The Head of AI Solutions is a newly created, executive-level position with a clear mandate: architect a unified AI strategy, build a world-class engineering team, and deliver production-grade AI at scale across the operational nerve center of a global bank. This is not a coordination or advisory role. It is a builder's role — one with the budget, the mandate, and the organizational reach to make it real.You will own the AI strategy and delivery capability across a $200M+ technology portfolio spanning some of the most operationally complex domains in banking: KYC, fraud detection, wholesale lending operations, global reconciliations, cash management, payments control, non-financial regulatory reporting, payroll, and international operations. The scale is significant, the problems are highly complex at this stage, and the impact is direct — the solutions you build will influence how trillions of dollars in transactions flow daily, how regulatory risk is managed, and how Citi's operational infrastructure evolves over the next decade.Unlike a role at a pure-play technology company, you will be solving AI challenges where failure has regulatory and systemic consequence — and where success reshapes the economics and resilience of critical global operations. The ambiguity is real, the stakes are high, and the opportunity for lasting impact is unmatched.This role reports directly to the Head of COO Technology.Responsibilities:AI Strategy & Platform Architecture:Define and own the multi-year AI strategy for the COO Technology portfolio; translate business intent into a concrete, milestone-driven execution roadmap with measurable outcomesDevelop architecture blueprints and end-to-end systems design for Generative AI and agentic workflows across diverse operational domainsBuild the shared AI platform — reusable models, tooling, guardrails, evaluation frameworks, and accelerators — that reduces duplication, lowers cost, and enables faster adoption across COOEstablish and enforce engineering standards, architectural guardrails, and development patterns across all AI initiativesDefine a rigorous total cost of ownership model for developing, deploying, and sustaining AI in productionIdentify and evaluate emerging GenAI technologies, foundation models, and agent frameworks — and make deliberate, defensible decisions on where to build, buy, or partnerProduction AI Delivery at Enterprise ScaleLead end-to-end delivery of AI solutions across high-complexity, regulated operational environments — from architecture through production deployment, monitoring, and continuous improvementDrive the agentic product development lifecycle: runtime harness design, evaluation frameworks, human-in-the-loop workflows, feedback loops, and production readiness criteriaManage cross-functional delivery spanning engineering, product, data, architecture, cyber, risk & compliance, and operationsProactively identify and resolve dependencies, critical path risks, and systemic delivery blockers; ensure on-time, on-budget executionEnsure all AI solutions meet production-grade standards: stability, scalability, auditability, explainability, and regulatory complianceExecutive Partnership & AI GovernanceServe as the senior AI executive point of contact for COO function leads — partnering directly with the Heads of Core Operations Technology, Shared Services Technology, and Controls TechnologyLead AI governance forums and Architecture Review Boards; establish clear decision rights and review cadences across the portfolioTranslate complex technical realities into clear, compelling narratives for senior non-technical audiences — including COO, CIO, and regulatory stakeholdersDevelop executive-level communications — steering committee materials, portfolio dashboards, and milestone tracking — that improve decision velocity and reduce execution riskEnsure full adherence to Citi's internal policies, risk and control frameworks, model risk management (MRM) standards, and applicable regulatory requirementsBuilding the AI Engineering OrganizationBuild, structure, and lead a high-performing AI engineering function aligned to COO's operational priorities — including team topology, operating model, and career pathwaysFoster a culture of technical excellence, intellectual curiosity, and pragmatic innovation: engineers who ship production solutions, not prototypesOwn and manage the AI technology portfolio budget (~$200M), driving disciplined funding allocation, financial transparency, and cost-to-serve accountabilityLead productivity and efficiency programs with measurable targets across cycle time, quality, throughput, and engineering leverageDefine and optimize vendor and partner strategy, including strategic AI platform partnerships (e.g., Google, Anthropic), third-party tooling, and outsourced delivery modelsQualifications:15+ years of experience in Technology - Required: Generative AI & LLM Engineering: Deep, hands-on expertise in large language models including model selection, fine-tuning, prompt engineering, retrieval-augmented generation (RAG), vector database design, and evaluation methodologies. You understand how models behave in production, not just in demos.Agentic Systems Design:Proven experience designing and deploying multi-agent architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool-use patterns, human-in-the-loop workflows, and agentic safety at enterprise scaleAI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such as MLflow, Vertex AI, or SageMakerCloud AI Platforms: Demonstrated deployment of AI workloads on AWS, GCP, and/or Azure including managed ML services, scalable inference infrastructure, and vector storesProgramming & Frameworks: Strong Python proficiency; working knowledge of PyTorch or TensorFlow; applied experience with AI application frameworks (LangChain, LlamaIndex, or equivalents)Enterprise AI Architecture: Designing AI systems for regulated production environments: data security, model explainability, audit logging, access controls, and integration with legacy systemsLeadership & Delivery - Required: 15+ years in technology, with a proven record of leading large-scale engineering organizations through build-out and transformation10+ years of management experience, including direct leadership of senior engineers and architects, and management of managers across global teamsDemonstrated delivery of enterprise AI solutions with realized measurable business outcomes — not just successful pilots or proofs of conceptExperience managing large, complex technology budgets ($50M+) with accountability for financial transparency and ROITrack record of operating effectively in matrixed, cross-functional organizations at the intersection of technology and operationsDomain & Contextual Knowledge - Strongly PreferredDeep familiarity with financial services operations and the regulatory landscape — particularly KYC/AML, fraud, reconciliations, and regulatory reportingStrong understanding of model risk management (MRM) and what it takes to move AI from development to production under regulatory scrutinyExperience engaging with strategic technology partners (cloud providers, AI platform companies) at an executive levelLeadership ProfileYou build platforms, not point solutions — you instinctively seek the reusable, the shared, the scalableYou are equally credible in a deep technical architecture review and a board-level strategy discussionYou attract, develop, and retain strong technical talent — engineers want to work for you because they growYou operate with clarity and urgency in ambiguous environments; complexity energizes rather than paralyzes youYou communicate with precision — you can make a complex AI architecture concept land with a CRO, a COO, and a principal engineer, and you do it differently for eachEducation:Bachelors required; Master's in CS, AI/ML, or related field preferredWhat success looks like: In the first year, we expect the successful candidate to:Establish the AI engineering team and operating model — hire and structure a high-performing team with clear roles, responsibilities, and a strong cultureDeliver 3+ production-grade agentic AI systems across priority COO domains, with measurable operational impact (cost, speed, quality, or risk reduction)Launch the shared AI platform — reusable RAG infrastructure, evaluation frameworks, and common tooling adopted across COO TechnologyDefine and align the multi-year AI roadmap with COO function leads and the Head of COO Technology, with clear prioritization, milestones, and funding allocationEstablish AI governance — Architecture Review Boards, model risk processes, and compliance frameworks embedded in the delivery lifecycleWhy this roleScale that is rare. You will build AI capabilities across one of the world's most operationally complex banking platforms — with direct, measurable impact on how trillions of dollars in transactions are processed, controlled, and reported daily.Greenfield mandate. The team, the platform, and the strategy are yours to define. You will set the architectural direction, the engineering culture, and the standards that govern AI across the COO portfolio.Uniquely hard problems. Banking operations at this scale generate AI challenges that simply do not exist elsewhere — legacy system integration, regulatory auditability requirements, multi-jurisdictional data