Head of AI Solutions, COO Technology - MD C16 - Citi in London

Discover your future at Citi. Working at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 dedicated people from around the globe. At Citi, you'll have the opportunity to grow your career, give back to your community and make a real impact.

Citi'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 unsolved at this level of complexity, 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 outcomes. Develop architecture blueprints and end-to-end systems design for Generative AI and agentic workflows across diverse operational domains. Build the shared AI platform - reusable models, tooling, guardrails, evaluation frameworks, and accelerators - that reduces duplication, lowers cost, and enables faster adoption across COO. Establish and enforce engineering standards, architectural guardrails, and development patterns across all AI initiatives. Define a rigorous total cost of ownership model for developing, deploying, and sustaining AI in production. Identify and evaluate emerging GenAI technologies, foundation models, and agent frameworks - and make deliberate, defensible decisions on where to build, buy, or partner.
• Production AI Delivery at Enterprise Scale: Lead end-to-end delivery of AI solutions across high-complexity, regulated operational environments - from architecture through production deployment, monitoring, and continuous improvement. Drive the agentic product development lifecycle: runtime harness design, evaluation frameworks, human-in-the-loop workflows, feedback loops, and production readiness criteria. Manage cross-functional delivery spanning engineering, product, data, architecture, cyber, risk & compliance, and operations. Proactively identify and resolve dependencies, critical path risks, and systemic delivery blockers; ensure on-time, on-budget execution. Ensure all AI solutions meet production-grade standards: stability, scalability, auditability, explainability, and regulatory compliance.
• Executive Partnership & AI Governance: Serve 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 Technology. Lead AI governance forums and Architecture Review Boards; establish clear decision rights and review cadences across the portfolio. Translate complex technical realities into clear, compelling narratives for senior non-technical audiences - including COO, CIO, and regulatory stakeholders. Develop executive-level communications - steering committee materials, portfolio dashboards, and milestone tracking - that improve decision velocity and reduce execution risk. Ensure full adherence to Citi's internal policies, risk and control frameworks, model risk management (MRM) standards, and applicable regulatory requirements.
• Building the AI Engineering Organization: Build, structure, and lead a high-performing AI engineering function aligned to COO's operational priorities - including team topology, operating model, and career pathways. Foster a culture of technical excellence, intellectual curiosity, and pragmatic innovation: engineers who ship production solutions, not prototypes. Own and manage the AI technology portfolio budget (~$200M), driving disciplined funding allocation, financial transparency, and cost-to-serve accountability. Lead productivity and efficiency programs with measurable targets across cycle time, quality, throughput, and engineering leverage. Define and optimize vendor and partner strategy, including strategic AI platform partnerships (e.g., Google, Anthropic), third-party tooling, and outsourced delivery models.

Qualifications:

• 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 scale.
• AI/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 SageMaker.
• Cloud AI Platforms: Demonstrated deployment of AI workloads on AWS, GCP, and/or Azure including managed ML services, scalable inference infrastructure, and vector stores.
• Programming & Frameworks: Strong Python proficiency; working knowledge of PyTorch.