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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.
The Opportunity
Citi is building the foundational AI infrastructure that will power thousands of products, teams, and client interactions across a global financial institution. We are seeking an exceptional Director\-level AI leader to define, build, and scale the enterprise\-wide AI strategy, and influence with enterprise strategy. Work with every business line to innovate with AI — safely, efficiently, and at scale.
This is an experienced individual in AI Strategist, platform\-mindset, enterprise\-scale role. You are not building one product for one client. You are directing the factory, the standards, and the what to build and where to take the organization towards that enables hundreds of product teams to build the right AI products in the right way. Your primary customer is the internal developer and product manager, and your success is measured by their adoption, velocity, and the reusability of your platform capabilities.
What Makes This Role Distinct
Unlike a business\-embedded product role, this position is defined by:
- A horizontal, enterprise\-wide mandate and Strategy— your strategy serves all business lines, not one
- Deep technical and architectural fluency in AI usage and AI platforms— you can work with all lines of business \-Payments, Trade, Custody, Funds, Issuer, Liquidity, understand what market is doing, what competition is doing. Understand client needs, client problems. Then write a AI strategy for Services Business that drives growth, client experience and operational efficiency
- A focus on research, competitive edge, technology forefront — writing the at par strategy with market and competitors
- The challenge of influencing without authority — persuading dozens of independent teams to adopt centralized tools and processes
Key Responsibilities
Define and Drive AI Strategy for Services Business
- Define and execute the enterprise AI strategy and roadmap — shaping the future execution path for core capabilities, business use case implementation and alignment with Enterprise Strategy, that drives business growth, client demand execution and drives key KPIs for Services business, the core platform strategy, and enterprise AI standards
- Work with cluster and country teams, drive the strategy wide and deep, define cluster, country, regulatory, client adoption and revenue generation using AI KPIs, track and drive the KPIs at product, business, cluster and country level
- Report the strategy adoption KPIs and metrics to Governance team and enterprise team, AI command Center portals
- Create Presentations, narrative, and present at various forums, town halls and regulatory reviews
Platform Oversight in line with Strategy, Enterprise alignment
- Oversee that platform design and capabilities that it is in alignment with the Strategy you write and pursue adoption across firm. Build once and reuse
- Manage communication and clear roadmap to a wide array of business, country, cluster and enterprise stakeholders
- Ensure alignment with cluster and country regulations.
- Partner with Risk, Compliance, Model Risk, and Architecture to ensure Strategy is line with policies and regulations
- Establish presentations, playbooks, and input into governance frameworks and help move ideas into execution and are aligned to strategy.
- Serve as the firm\-wide Services AI strategy and platform evangelist — inspiring business lines to build on the platform and demonstrating the value of reuse, input into centralized AI capabilities and oversee adoption
- Have experience to know when to build vs use vendor solution or vendor partnership.
Cross\-Team Leadership
- Work and influence cross\-business and enterprise partnerships, collaboration, work with technical and architecture teams.
- Research, across markets, competitors
- Navigate a complex matrixed organization — influencing dozens of teams and stakeholders to write and adopt Services AI Strategy.
Who We're Looking For
Required
- 12\+ years of product management, platform strategy, or enterprise technology leadership, with demonstrated experience delivering complex, enterprise\-scale platforms
- Proven experience defining and implementing AI, GenAI, or Agentic AI solutions, including translating business problems into scalable, reusable platform capabilities
- Deep understanding of AI concepts — definition, where AI is better used vs just technical development, orchestration, agentic, workflow automation, human\-in\-the\-loop.
- Preferred platform and vendor solution knowledge on LLMs and SLMs— Cloud solutions, APIs, cloud\-native architectures, reusable platform services, vendor solutions
- Demonstrated history of creating and enforcing and driving an AI, ML strategy
- Outstanding stakeholder management and executive communication skills
- Ability to influence product, technology, engineering, operations, risk, and senior business leaders across a highly matrixed organization
Preferred
- Experience delivering enterprise AI Strategy, drive major inputs into platform development
- Familiarity with LLMs, SLMs, prompt engineering, evaluation frameworks, and AI observability tooling
- Experience working with business Product teams, AI engineering teams, COO, Risk, Enterprise technology and driving KPIs and business OKRs
- Financial services, payments, treasury, or transaction banking experience is preferred but not must. However, strategy you write should be usable and applicable to these business
Success Measures
- Successful Strategy that resonates with Business and enterprise teams
- KPIs adoption — Defined KPIs are contributing to different business OKRs, well understood by teams including cluster and country KPIs.
- Reusability — Strategy and capabilities are adopted across business, enterprise and capabilities are central enough for other business than Services can also use it
- Oversight from use case discovery to production deployment, in line with Strategy and are mapping to OKRs and KPIs
- Client Satisfaction KPIs, Business growth KPIs, Use case implementation KPIs, Regulatory management and reporting KPIs, Presentation material creation for Senior management, executive presentation in Townhall, MBR, QBR KPIs
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Job Family Group:
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Product Management and Development
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Job Family:
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Product Management
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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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$200,000\.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 $200K-$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 ($250K) sits 16% above the category median. Disclosed range: $200K 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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