AI Product Strategy & Agentic Solutions, Liquidity Management - Director

📍 Job Overview

Job Title: AI Product Strategy & Agentic Solutions, Liquidity Management - Director

Company: Citi

Location: 388 GREENWICH STREET - TOWER, New York, New York, United States

Job Type: Full time

Category: Product Management & Development / AI Strategy

Date Posted: 2026-09-11

Experience Level: 12+ years

Remote Status: Hybrid

🚀 Role Summary

• Lead the strategic vision and execution of AI and Agentic AI initiatives within Citi's global Liquidity Management business.

• Drive the development and deployment of production-ready Generative AI and agentic AI solutions that enhance liquidity products, services, and client interactions.

• Act as a key strategic liaison between Liquidity Management, Treasury, Product, Technology, Data & AI, Operations, and Risk teams to identify and prioritize high-impact AI opportunities.

• Focus on achieving tangible commercial outcomes, including revenue growth, cost savings, and competitive differentiation through AI-driven product innovation and operational efficiencies.

📝 Enhancement Note: This role is positioned as a strategic leadership position focused on AI product strategy within a specific business domain (Liquidity Management), rather than a pure AI engineering or data science role. The emphasis is on understanding the business context, identifying opportunities, and driving commercial outcomes.

📈 Primary Responsibilities

• Own and execute the AI and Agentic AI strategy for the Liquidity Management business, establishing a multi-year roadmap for Generative AI, advanced analytics, and intelligent automation.

• Identify, prioritize, and lead initiatives from business problem identification through AI design, proof of concept, production deployment, and user adoption.

• Advise senior Liquidity and Treasury leadership on emerging AI capabilities and their potential business impact, informing strategic decision-making.

• Develop and implement scalable AI patterns, frameworks, and capabilities that can be deployed across multiple liquidity products and processes globally.

• Partner with product and business leaders to apply deep knowledge of liquidity management products (e.g., cash management, deposits, liquidity concentration, intraday liquidity, liquidity forecasting, working capital solutions, treasury management) to AI-driven transformation opportunities.

• Lead the design and implementation of enterprise-grade AI agents and multi-agent workflows, defining appropriate levels of autonomy and human oversight.

• Establish robust controls around AI accuracy, hallucination, data access, authorization, privacy, and model risk, ensuring alignment with Citi's enterprise risk standards.

• Proactively partner with Legal, Risk, Compliance, and Model Risk teams to navigate the regulatory landscape and ensure responsible AI deployment.

• Serve as a trusted advisor to senior executives, presenting AI strategy, investment cases, business outcomes, and risks to senior management.

• Build strong, influential partnerships across Technology, Data, AI/ML, Product, Operations, Risk, Compliance, Legal, and Cybersecurity.

• Drive adoption and manage change management initiatives across the organization to ensure successful integration of AI solutions.

📝 Enhancement Note: The responsibilities clearly delineate a blend of strategic product leadership, deep business domain expertise in liquidity management, and practical application of advanced AI technologies, particularly agentic AI. The emphasis on risk, governance, and regulatory compliance is critical for a financial services role.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Bachelor's degree in a relevant field such as Finance, Economics, Computer Science, Engineering, or a related business discipline is typically expected for a Director-level role in financial services product strategy. A Master's degree or MBA would be highly advantageous.

Experience:

• 12+ years of product management or product leadership experience, with a demonstrated track record of delivering large-scale, technology-led products.

• 5+ years of direct or indirect external client-facing experience, with proven ability in co-creating and validating products with enterprise clients.

• Demonstrated P&L ownership or direct accountability for a key business metric significantly improved through product launches. Required Skills:

• Deep, demonstrated knowledge of bank liquidity products and solutions (e.g., cash management, deposits, liquidity concentration, sweeps, intraday liquidity, liquidity forecasting, working capital, treasury management).

• Proven experience working directly with liquidity products, Treasury solutions, client liquidity structures, or related banking businesses.

• Demonstrated ability to translate complex banking problems into practical AI solution architectures.

• Proven experience developing and deploying Generative AI and/or agentic AI solutions.

• Strong understanding of AI governance, model risk management, responsible AI, and financial services regulatory requirements.

• Exceptional stakeholder management skills: ability to build consensus, influence without authority, and operate effectively in a matrixed global organization.

• Outstanding communication skills: ability to translate complex AI concepts for executive, technical, and client audiences.

• Strategic planning and business transformation capabilities.

• Analytical and problem-solving skills with a data-driven approach. Preferred Skills:

• Experience with AI agents and multi-agent workflow design and implementation.

• Familiarity with advanced analytics, machine learning, and intelligent automation techniques.

• Experience in driving adoption and managing change within large organizations.

• Knowledge of cybersecurity principles relevant to AI deployments.

• Experience in product development lifecycle management within a financial services context.

📝 Enhancement Note: The requirements emphasize a unique combination of deep financial product knowledge (liquidity management) and cutting-edge AI expertise (Generative AI, Agentic AI), along with strong leadership and client-facing skills. The explicit mention of P&L ownership and external client engagement highlights the commercial focus of the role.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Candidates should be prepared to showcase a portfolio demonstrating their experience in developing and launching complex, technology-driven products, particularly those involving AI or advanced analytics.

• Specific examples of how they have translated business needs into product strategies and roadmaps, leading to measurable business impact (revenue generation, cost savings, efficiency improvements).

• Case studies detailing their involvement in the end-to-end product lifecycle, from ideation and design to production deployment and adoption, with a focus on AI solutions.

• Evidence of experience in managing cross-functional teams, influencing stakeholders, and navigating complex organizational structures to deliver product initiatives.

• Documentation or examples of how they have managed product risks, ensured regulatory compliance, and implemented governance frameworks for technology products. Process Documentation:

• Examples of how they have documented and optimized workflows for product development and deployment, particularly for AI-driven solutions.

• Evidence of experience in defining and implementing processes for AI governance, model risk management, and responsible AI deployment.

• Demonstrations of how they have established scalable AI patterns, frameworks, and capabilities that can be reused across different products and business units.

• Materials illustrating their approach to client co-creation, validation, and the integration of client feedback into product development processes.

📝 Enhancement Note: While not explicitly stated as a "portfolio requirement" in the job description, the nature of the role and its emphasis on strategy, execution, and commercial outcomes necessitates a portfolio that highlights past successes in product leadership, AI implementation, and business impact. The focus should be on demonstrating strategic thinking, problem-solving, and tangible results.

💵 Compensation & Benefits

Salary Range: Based on the provided information for "Primary Location Full Time Salary Range," the estimated salary range for this Director-level position in New York, NY is $170,000 to $300,000 USD per year.

📝 Enhancement Note: This salary range is explicitly provided in the job posting for the primary location and reflects a Director-level position in a high-cost-of-living area like New York City within the financial services industry, particularly for a specialized role involving AI and product strategy. The range accounts for the extensive experience (12+ years) and leadership responsibilities.

Benefits:

• Comprehensive medical, dental, and vision coverage.

• 401(k) retirement savings plan.

• Life, accident, and disability insurance.

• Wellness programs.

• Generous paid time off (PTO), including planned time off (vacation) and unplanned time off (sick leave).

• Paid holidays.

• Potential for discretionary and formulaic incentive and retention awards. Working Hours:

• The role is listed as "Full time," which typically implies a standard 40-hour work week. However, given the Director level and the demanding nature of AI strategy and product development in financial services, longer hours may be expected, especially during critical project phases or deadlines. The hybrid work arrangement suggests flexibility.

📝 Enhancement Note: The benefits listed are standard for large financial institutions like Citi and are designed to attract and retain senior talent. The mention of incentive and retention awards highlights the performance-driven nature of the role.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services (Banking, Treasury and Securities Services)

Company Size: Citi is a global financial institution with tens of thousands of employees worldwide, indicating a large, complex, and highly regulated operating environment. This size implies extensive resources, established processes, and a broad network of potential collaborators and stakeholders.

Founded: Citi's origins trace back to 1812, signifying a long history and deep-rooted presence in the global financial landscape. This heritage suggests a culture that values stability, trust, and long-term relationships, while also adapting to technological advancements and market changes.

Team Structure:

• The role operates within the Liquidity Management business line, which is part of Citi's broader Services division.

• This position will lead a matrixed, global product team, requiring collaboration across multiple regions, functions (Product, Technology, Data & AI, Operations, Risk, Compliance, Legal), and regulatory environments.

• The Director will serve as a strategic advisor to senior leadership within Liquidity Management and Treasury, indicating a high level of visibility and influence. Methodology:

• Data Analysis & Insights: The role necessitates a strong emphasis on data analysis to identify opportunities, measure impact, and inform AI strategy. This includes analyzing client liquidity positions, balances, transaction activity, and trends.

• Workflow Planning & Optimization: A core responsibility is to identify and implement AI-driven improvements to existing workflows, aiming to reduce manual processes and operational complexity.

• Automation & Efficiency: The focus on Generative AI and agentic AI directly supports the goal of automating complex tasks and driving efficiency across liquidity management processes.

Company Website: https://www.citigroup.com/

📝 Enhancement Note: Citi's established position in the financial industry means operations professionals can expect a structured environment with a strong emphasis on risk management, compliance, and established best practices. The company's global scale offers opportunities for diverse project involvement and cross-cultural collaboration.

📈 Career & Growth Analysis

Operations Career Level: This role is at the Director level, signifying a senior leadership position responsible for strategic direction, product innovation, and significant business impact within a specialized domain (AI in Liquidity Management). It requires extensive experience and the ability to influence at executive levels.

Reporting Structure: The Director will report to senior leadership within the Liquidity Management and Treasury functions. They will lead a matrixed global product team, meaning they will have indirect reports and influence across various departments rather than direct line management of all individuals involved.

Operations Impact: This role has a direct impact on the commercial success and operational efficiency of Citi's Liquidity Management business. By leveraging AI, the Director will aim to:

• Drive revenue growth through enhanced product offerings and client advisory capabilities.

• Achieve significant cost savings by automating manual processes and reducing operational complexity.

• Enhance competitive differentiation by offering cutting-edge AI-powered solutions.

• Improve client satisfaction and engagement through intelligent insights and personalized solutions. Growth Opportunities:

• Operations Skill Advancement: Deepen expertise in AI product strategy, Generative AI, agentic AI, and their application within complex financial services environments. Gain hands-on experience in driving enterprise-wide AI transformation.

• Leadership Development: Develop strong leadership capabilities in managing matrixed, global teams, influencing senior stakeholders, and driving strategic initiatives within a large, complex organization.

• Specialization & Expansion: Potential to expand influence across other Treasury and Securities Services product lines or to move into broader AI strategy leadership roles within Citi. Opportunity to become a recognized expert in AI for financial services.

• Industry Influence: Contribute to shaping the future of AI in banking and liquidity management, potentially leading to industry recognition and thought leadership.

📝 Enhancement Note: This Director-level role offers significant strategic influence and the opportunity to pioneer AI adoption in a critical business area. Growth opportunities are geared towards deepening AI and financial product expertise, as well as developing advanced leadership and strategic management skills within a global financial institution.

🌐 Work Environment

Office Type: The role is based at Citi's 388 Greenwich Street location in New York, NY, indicating an on-site presence within a major corporate office environment. The "Hybrid" work arrangement suggests a blend of in-office and remote work.

Office Location(s): The primary location is 388 Greenwich Street, New York, NY. Citi has numerous offices globally, and this role involves leading a global team, suggesting potential for virtual collaboration with colleagues in other regions.

Workspace Context:

• Collaborative Environment: Working in a large corporate office in New York City provides access to a vibrant professional ecosystem and opportunities for in-person collaboration with colleagues from various departments, including Technology, Product, Data, and Risk.

• Operations Tools & Technology: Access to Citi's extensive technology infrastructure, including advanced analytics platforms, AI/ML tools, and communication systems, will be crucial for developing and deploying AI solutions.

• Team Interaction: The hybrid model allows for focused in-office collaboration sessions, strategic planning meetings, and client engagements, balanced with remote work flexibility for individual productivity and deep work.

Work Schedule:

• The role is full-time, with a standard 40-hour work week expected. However, the hybrid nature and the strategic, leadership-oriented responsibilities may require flexibility to accommodate global team coordination, client needs, and project deadlines. This includes potential for early morning or late afternoon calls with international teams.

📝 Enhancement Note: The hybrid work environment at a major financial hub like New York City offers a dynamic professional setting. Operations professionals can expect a structured yet flexible work arrangement, with ample opportunities for collaboration and access to advanced technological resources.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Likely conducted by a recruiter to assess basic qualifications, experience, and alignment with the role's core requirements, particularly in liquidity management and AI.

• Hiring Manager Interview: In-depth discussion with the hiring manager to evaluate strategic thinking, product vision, leadership style, and fit within the team.

• Panel Interviews: Interviews with key stakeholders from Technology, Data & AI, Product, Risk, and potentially senior business leaders. These will focus on technical depth, AI knowledge, business acumen, stakeholder management capabilities, and understanding of regulatory/risk frameworks.

• Case Study/Presentation: Candidates may be asked to prepare and present a strategic proposal, a product roadmap, or a solution to a hypothetical AI challenge within liquidity management. This assesses their ability to articulate strategy, analyze problems, and propose actionable solutions.

• Final Round: May involve senior executive interviews to assess overall fit, strategic alignment, and leadership potential.

Portfolio Review Tips:

• Quantify Impact: For any presented case studies or project examples, focus on quantifiable results. Use metrics like revenue generated, cost savings achieved, efficiency gains (e.g., reduction in manual processing time), or client adoption rates.

• Highlight AI & Liquidity Expertise: Ensure your portfolio clearly demonstrates your deep understanding of liquidity management products and solutions, alongside your experience in developing and deploying Generative AI and/or agentic AI.

• Showcase Strategic Thinking: Present your thought process for identifying opportunities, developing strategies, defining roadmaps, and prioritizing initiatives. Explain the "why" behind your decisions.

• Demonstrate Collaboration & Leadership: Provide examples of how you have led cross-functional teams, managed diverse stakeholders, influenced senior leadership, and driven consensus in complex environments.

• Address Risk & Governance: Include examples of how you have managed product risks, ensured regulatory compliance, and implemented responsible AI governance frameworks.

Challenge Preparation:

• AI Strategy Articulation: Be prepared to discuss your vision for AI in liquidity management, including potential use cases, strategic priorities, and a roadmap for implementation.

• Problem-Solving Scenarios: Practice analyzing complex business problems related to liquidity, forecasting, or client advisory, and proposing AI-driven solutions. Consider potential challenges and mitigation strategies.

• Stakeholder Management Scenarios: Prepare examples of how you would manage conflicts, build consensus, and influence stakeholders with differing priorities.

• Liquidity Product Deep Dive: Refresh your knowledge of key liquidity management concepts, products, and industry trends. Be ready to discuss how AI can transform these areas.

📝 Enhancement Note: The interview process is designed to rigorously assess strategic thinking, deep domain expertise, AI proficiency, leadership capabilities, and risk management acumen. A well-curated portfolio that emphasizes quantifiable results and strategic impact will be crucial for success.

🛠 Tools & Technology Stack

Primary Tools:

• AI/ML Platforms: Experience with cloud-based AI/ML platforms (e.g., AWS SageMaker, Google AI Platform, Azure Machine Learning) for developing, training, and deploying models.

• Generative AI Frameworks: Familiarity with frameworks and tools for Generative AI development (e.g., LangChain, Hugging Face Transformers, OpenAI API).

• Agentic AI Frameworks: Experience with tools and methodologies for building and managing AI agents and multi-agent systems.

• Data Science & Analytics Tools: Proficiency in tools like Python (with libraries like Pandas, NumPy, Scikit-learn), R, or similar for data manipulation, analysis, and model development.

Analytics & Reporting:

• Business Intelligence (BI) Tools: Experience with BI platforms such as Tableau, Power BI, or Qlik for data visualization, dashboard creation, and reporting on AI initiative performance.

• Data Warehousing & ETL Tools: Understanding of data warehousing concepts and experience with ETL (Extract, Transform, Load) processes and tools for data preparation.

• Statistical Analysis Software: Familiarity with tools for statistical modeling and analysis.

CRM & Automation:

• CRM Systems: While not a primary focus, understanding how AI solutions integrate with or augment CRM functionalities for client management and sales enablement would be beneficial.

• Workflow Automation Tools: Experience with platforms that facilitate the creation and management of automated workflows, especially those involving AI agents.

• API Integration Tools: Knowledge of how to integrate different systems and services using APIs.

📝 Enhancement Note: The technology stack is heavily oriented towards AI/ML development, data science, and business intelligence. Proficiency in cloud-based AI platforms, Generative AI frameworks, and data analysis tools will be essential. The ability to integrate these solutions with existing banking systems and CRM platforms is also key.

👥 Team Culture & Values

Operations Values:

• Customer-Centricity: A strong focus on understanding and meeting the complex needs of institutional clients in liquidity management, leveraging AI to deliver enhanced value and client experience.

• Innovation & Agility: Embracing cutting-edge AI technologies like Generative and Agentic AI to drive product innovation and adapt quickly to market changes, while maintaining robust governance.

• Data-Driven Decision Making: Utilizing data analytics and AI-driven insights to inform strategy, prioritize initiatives, and measure the impact of AI solutions on business outcomes.

• Risk & Responsibility: Upholding the highest standards of responsible AI, governance, model risk management, and regulatory compliance, reflecting Citi's commitment to a secure and ethical operating environment.

• Collaboration & Partnership: Fostering strong, cross-functional partnerships across business, technology, data, risk, and compliance teams to ensure successful AI strategy execution and adoption.

Collaboration Style:

• Cross-functional Integration: The role requires extensive collaboration with diverse teams, including Product Development, Technology, Data Science/AI/ML, Operations, Risk, Compliance, and Legal. Effective communication and consensus-building are paramount.

• Process Review & Feedback: A culture that encourages continuous improvement and open feedback on AI solutions, product designs, and strategic approaches, both internally and with clients.

• Knowledge Sharing: Encouraging the sharing of insights, best practices, and lessons learned across teams and regions to accelerate AI adoption and foster innovation throughout the organization.

📝 Enhancement Note: Citi's culture likely emphasizes a balance between innovation and rigorous risk management, typical of large financial institutions. Operations professionals should be prepared for a highly collaborative environment that values data-driven insights, ethical AI deployment, and strong stakeholder engagement.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Complex Regulations: Operating within a highly regulated financial services environment requires meticulous attention to AI governance, model risk, data privacy, and compliance, which can slow down development and deployment.

• Integrating AI into Legacy Systems: Integrating advanced AI solutions with existing, potentially complex, and older banking systems can present significant technical and operational hurdles.

• Driving AI Adoption & Change Management: Gaining buy-in and driving widespread adoption of new AI capabilities across a global organization with diverse user groups and established workflows can be challenging.

• Balancing Innovation with Risk Mitigation: The inherent tension between pushing the boundaries of AI innovation and ensuring robust risk controls and responsible deployment requires careful strategic management.

• Talent Acquisition & Development: Attracting and retaining top AI talent, especially those with deep financial services domain knowledge, can be competitive.

Learning & Development Opportunities:

• AI Specialization: Opportunities to deepen expertise in cutting-edge AI fields such as Generative AI, Large Language Models (LLMs), and advanced agentic AI architectures.

• Financial Services Domain Expertise: Further developing a nuanced understanding of complex banking products, treasury operations, and client needs within the global financial markets.

• Strategic Leadership & Influence: Enhancing skills in executive communication, stakeholder management, influencing without authority, and driving large-scale strategic initiatives within a global enterprise.

• Industry Conferences & Certifications: Potential to attend leading AI and FinTech conferences, pursue relevant certifications, and engage with industry thought leaders.

• Mentorship: Opportunities to be mentored by senior leaders within Citi and to mentor junior team members, fostering leadership development.

📝 Enhancement Note: The challenges in this role are typical of pioneering advanced technologies within established financial institutions. Growth opportunities are significant, focusing on developing specialized AI and domain expertise, coupled with advanced strategic leadership skills.

💡 Interview Preparation

Strategy Questions:

• "Describe your vision for how AI, particularly Generative AI and agentic AI, can transform liquidity management at a global bank like Citi. What are the top 2-3 opportunities you would prioritize and why?" (Preparation: Articulate a clear, data-informed vision. Prioritize based on business impact, feasibility, and alignment with Citi's strategic goals.)

• "How would you approach building consensus and securing sponsorship for a high-impact AI initiative from senior leadership across different business units (e.g., Treasury, Product, Technology, Risk)?" (Preparation: Focus on stakeholder mapping, value proposition articulation, risk mitigation strategies, and building strong business cases.)

• "Imagine a scenario where an agentic AI solution for liquidity forecasting produces an inaccurate prediction leading to a potential liquidity shortfall. How would you design the governance and oversight mechanisms to prevent and mitigate such issues?" (Preparation: Detail your understanding of model risk management, human-in-the-loop processes, fail-safes, monitoring, and incident response protocols.) Company & Culture Questions:

• "What do you know about Citi's Liquidity Management business and the challenges it faces in today's market? How do you see AI fitting into its future?" (Preparation: Research Citi's Services division, Liquidity Management products, and recent industry trends. Connect AI capabilities to specific business needs and challenges.)

• "How do you foster a culture of innovation and responsible AI adoption within a large, global organization that has established risk and compliance frameworks?" (Preparation: Discuss strategies for balancing innovation with governance, encouraging experimentation, and driving change management.)

• "Describe a time you had to influence stakeholders who were resistant to adopting a new technology or process. What was your approach and what was the outcome?" (Preparation: Use the STAR method to detail a situation involving resistance, your strategy to overcome it, and the measurable result.) Portfolio Presentation Strategy:

• Structure: Organize your presentation logically, starting with the business problem, your strategic approach, the AI solution developed, the implementation process, and most importantly, the quantifiable business impact and lessons learned.

• Quantify Everything: For each case study, use concrete numbers and metrics to demonstrate ROI, cost savings, efficiency gains, or revenue impact.

• Focus on Strategy & Execution: Clearly articulate your strategic thinking behind the product decisions and the execution steps you took to bring the AI solution to life.

• Highlight Collaboration: Emphasize how you worked with cross-functional teams and managed stakeholders to achieve success.

• Address Risk & Governance: Be prepared to discuss how you navigated regulatory requirements and managed risks associated with your AI deployments.

• Tailor to Citi: Where possible, subtly tailor your examples to align with Citi's business, its scale, and its commitment to responsible innovation.

📝 Enhancement Note: Interview preparation should focus on demonstrating a deep understanding of both AI strategy and the intricacies of financial services, particularly liquidity management. The ability to articulate complex ideas clearly, back them with data, and showcase strong leadership and risk management capabilities will be critical.

📌 Application Steps

To apply for this operations position:

• Submit your application through the official Citi careers portal via the provided URL.

• Operations Portfolio Customization: Curate a selection of your most relevant projects that showcase your experience in product strategy, AI/ML implementation (specifically Generative or Agentic AI), and driving commercial outcomes in financial services. Quantify achievements with metrics and highlight your understanding of liquidity management products.

• Resume Optimization: Ensure your resume clearly articulates your 12+ years of product management experience, deep expertise in bank liquidity products, and demonstrable success with AI-driven product launches. Use keywords from the job description such as "AI Product Strategy," "Agentic AI," "Liquidity Management," "Generative AI," "Stakeholder Management," and "P&L Ownership."

• Interview Preparation: Practice articulating your AI strategy for financial services, discussing complex case studies that demonstrate problem-solving and leadership, and preparing to answer questions about risk management and regulatory compliance in AI. Rehearse your portfolio presentation to ensure clarity, conciseness, and impact.

• Company Research: Thoroughly research Citi's Liquidity Management services, its current AI initiatives (if publicly available), its corporate values, and its approach to innovation and risk management. Understand the competitive landscape for Treasury and Securities Services.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.