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About This Role
Discover your future at Citi
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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.
Job Overview
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Services provides global solutions that help corporations, financial institutions, public sector and commercial clients optimize operations and drive their business forward. Through our five business lines – Liquidity Management Services, Payments, Trade \& Working Capital Solutions, Investor Services and Issuer Services \- we provide cash management, payments/receivables solutions, working capital solutions, post\-trade securities services and issuer services across Citi’s global network.
Own and Shape Citi Services’ Universal Chat AI Agenda \- Lead the strategy, development, and global deployment of Citi's Universal Chat Agent platform, building intelligent conversational experiences that transform how employees, operations teams, and clients access information, complete work, and interact with Citi Services.
About the Initiative
Background
Platform and Data Services is a strategic enabler across Citi, providing client and internal platforms, shared services, data capabilities, and operational infrastructure that support employees and clients globally. As Citi accelerates its AI transformation, conversational AI and agentic experiences have become a critical component of the firm's digital strategy. Universal Chat Agent serves as the central engagement layer connecting users with enterprise knowledge, workflows, platforms, and services through natural language interactions. The platform is designed to deliver secure, scalable, AI\-powered experiences that improve productivity, simplify complex processes, increase operational efficiency, and enhance client and employee experiences across the globe.
The Director, AI Product Manager will serve as the strategic and execution leader responsible for defining and scaling the Universal Chat ecosystem, driving adoption of AI\-powered assistants, agentic workflows, and intelligent automation capabilities throughout Citi.
Strategic Mandate
- Develop and Implement Common Conversational AI Platform
Define and execute the vision for Citi Services' Universal Chat Agent ecosystem, creating a unified AI\-powered experience that enables employees and clients to access information, knowledge, services, and workflows through conversational interfaces.
- Agentic AI \& Intelligent Automation
Drive the development of AI agents capable of orchestrating operational workflows, retrieving information, automating tasks, and assisting users with increasingly complex business processes while maintaining appropriate governance and controls.
- Employee Productivity \& Operational Transformation
Identify and deliver AI\-powered capabilities that reduce manual work, accelerate decision\-making, improve service delivery, and enhance productivity across Operations, Technology, Risk, Compliance, and Business teams.
- Client Experience Innovation
Partner across businesses to develop conversational experiences that simplify client interactions, improve self\-service capabilities, and create differentiated digital experiences.
- Responsible AI \& Enterprise Controls
Ensure all AI capabilities are developed in accordance with Citi's enterprise governance standards, data controls, model risk requirements, security policies, and regulatory obligations.
What You'll Do
Responsibilities
- DEFINE \& EXECUTE THE Common CHAT STRATEGY
Own the global product vision and roadmap for Universal Chat Agent. Define priorities, use cases, and investment strategies while balancing innovation, scalability, business value, and adoption goals.
- BUILD AI AGENTS
Lead the design and deployment of intelligent agents capable of executing tasks, accessing systems, orchestrating workflows, and delivering actionable insights through conversational interfaces.
- DRIVE AI\-POWERED EMPLOYEE EXPERIENCES
Develop AI solutions that improve employee productivity by providing intelligent knowledge retrieval, workflow assistance, operational guidance, case support, policy access, and decision support capabilities.
- DELIVER OPERATIONAL TRANSFORMATION
Identify and implement AI\-driven opportunities to simplify processes, reduce manual effort, automate repetitive activities, and improve service quality across shared services and operations functions.
- CREATE A CONNECTED ECOSYSTEM
Partner with platform and product owners and technology teams to integrate Universal Chat Agent with applications, knowledge repositories, workflow platforms, digital assistants, and operational systems.
- EMBED RESPONSIBLE AI \& GOVERNANCE
Ensure solutions comply with enterprise standards for model governance, explainability, privacy, security, data management, auditability, and regulatory compliance.
- BUILD \& MANAGE A WIDE STAKEHOLDER ECOSYSTEM
Develop strong partnerships across Technology, Operations, Product, Architecture, Data, Risk, Compliance, Legal, HR, and business leadership teams to drive alignment and successful delivery.
- DRIVE AGILE \& OUTCOMES\-LED EXECUTION
Lead cross\-functional teams to rapidly deliver high\-impact capabilities using agile methodologies, experimentation frameworks, and measurable business outcomes.
- LEAD ADOPTION \& CHANGE MANAGEMENT
Develop strategies that drive user adoption, AI literacy, organizational readiness, and sustainable behavioral change across global organizations.
- SERVE AS THE AI \& CHAT PLATFORM EVANGELIST
Represent Universal Chat Agent with senior leadership, governance forums, technology partners, and business stakeholders while championing Citi's AI transformation agenda.
Beneficial Skills \& Qualifications
Experience \& Qualifications
The ideal candidate combines deep product leadership experience, AI expertise, platform knowledge, and strong executive presence.
Required
- 10\+ years of product management experience with delivery of large\-scale technology platforms.
- Proven experience building, launching, or scaling AI, Generative AI, conversational AI, agentic AI, or intelligent automation products.
- Strong understanding of large language models, retrieval\-augmented generation (RAG), knowledge management, and AI orchestration frameworks.
- Demonstrated experience developing enterprise software products with measurable business outcomes and adoption targets.
- Strong commercial and strategic mindset with experience building business cases and prioritizing investments based on value realization.
- Experience leading complex cross\-functional initiatives across product, technology, operations, risk, and business organizations.
- Deep understanding of AI governance, model risk management, security, privacy, and responsible AI principles.
- Exceptional stakeholder management skills with the ability to influence senior executives in a global matrix environment.
- Outstanding verbal and written communication skills with the ability to translate complex technical concepts into business value.
- Demonstrated ability to operate effectively in ambiguous, fast\-moving environments while delivering measurable results at scale.
Preferred
- Experience deploying copilots, agent\-assist solutions, knowledge assistants, or conversational AI platforms.
- Experience integrating AI solutions with workflow, ticketing, case management, CRM, productivity, or operational platforms.
- Familiarity with agentic AI architectures, multi\-agent systems, and enterprise automation frameworks.
- Experience leading enterprise\-wide change management and AI adoption programs.
- Financial services, operations, shared services, or large\-scale regulated industry experience.
- Experience working directly with Risk, Compliance, Legal, and Information Security teams on AI deployments.
- Agile product leadership and experience managing global product teams across multiple regions.
Success Measures
- Adoption and active usage of Universal Chat Agent across Services.
- Productivity gains and operational efficiency improvements delivered through AI capabilities.
- Reduction in manual effort and service resolution times.
- Expansion of AI agent and workflow automation capabilities.
- Delivery of measurable business value while maintaining strong governance and risk controls.
- Increased employee satisfaction and improved digital experience outcomes.
What We Offer
At Citi, you will have the opportunity to shape AI at a truly global scale — working at the center of a firm\-wide transformation with the resources, reach, and leadership backing to deliver meaningful impact. This is a role with real ownership, strategic influence, and the chance to build something that redefines how a global institution works.
- Strategic ownership of a flagship AI platform with global reach and executive visibility across Citi.
- Hybrid working model — 3 days in the office and 2 days working remotely, providing flexibility alongside meaningful in\-person collaboration.
- Access to Citi's global network of technology, product, and AI professionals, with opportunities to collaborate across regions and business lines.
- Continuous learning and professional development, with exposure to cutting\-edge AI capabilities and enterprise\-scale product challenges.
- A performance\-driven environment that rewards delivery, innovation, and measurable business impact.
- Comprehensive financial wellbeing support, including competitive compensation and long\-term financial benefits.
- Wellbeing and work\-life balance support, with programs designed to help you perform at your best both professionally and personally.
Apply now to lead Citi's conversational AI agenda and build the platform that will define how a global financial institution works, serves, and innovates through AI.
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Job Family Group:
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Institutional Sales
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Job Family:
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Corporate Access
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Time Type:
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Primary Location:
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New York New York United States
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Primary Location Full Time Salary Range:
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$170,000\.00 \- $300,000\.00
In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental \& vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.
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Most Relevant Skills
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Please see the requirements listed above.
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Other Relevant Skills
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For complementary skills, please see above and/or contact the recruiter.
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Anticipated Posting Close Date:
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Aug 19, 2026
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Automated Processing and AI
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We use automated processing, including artificial intelligence, for our legitimate business interests (or our reasonable and appropriate business purposes) to identify and align the candidate's skills and abilities with a specific job opening. Additionally, if you so choose, or consent, we can match your skills and abilities to other suitable roles at Citi.
Importantly, all our hiring processes and decisions, including determining your suitability for a role, are conducted, checked, and decided by individuals. Our automated processing and AI do not involve relying on automatic or autonomous decision\-making. Please refer to any Jurisdictional Considerations, with specific provisions for your country (where relevant) for further details.
Illinois residents – AI Notice and Right
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*Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.*
Salary Context
This $170K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Citi, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($235K) sits 9% above the category median. Disclosed range: $170K to $300K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Citi AI Hiring
Citi has 32 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Agent Developer, AI Software Engineer. Positions span New York, NY, US, Tampa, FL, US, Jacksonville, FL, US. Compensation range: $170K - $300K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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