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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 more than 230,000 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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Citi is establishing a new, unified firmwide AI organisation under a newly created Group Head of Artificial Intelligence — merging the firm's Head of AI and CTO responsibilities into a single executive accountable for AI\-led transformation across every business and function. This post\-merger structure is intentionally distinct from prior models and will be the engine through which Citi competes in the AI era.
The COO for Group AI is the operational backbone of this new organization. As a critical partner to the Group Head of AI, this leader will drive strategic execution, own the operating model, hold the budget and ROI narrative, and partner with HR on the most consequential workforce transformation in the firm's recent history. This is a rare C\-16 mandate to stand up \- and run \- the operating engine of one of the most consequential transformations in global financial services.
Key Responsibilities
- Strategic Planning \& Business Execution
- Partner with the Group Head of AI and the pillar leads to establish actionable team wide operating plans, OKRs, and KPIs for the Group AI organization.
- Drive end\-to\-end program execution across all seven AI pillars \- from AI Strategy and Responsible AI through to the Core Platform, Agent Factory, and AI\-First Development \- ensuring delivery on time, within budget, and to required quality standards.
- Own the OM/AI workstream: design, refine, and continuously evolve the operating model for the new Group AI organization, including governance forums, decision rights, and cross\-pillar coordination.
- Identify opportunities for process improvement and operational efficiencies across the AI development, deployment, and adoption lifecycle.
- Financial Management \& ROI Ownership
- Act as end\-to\-end owner of the Group AI budget and investment portfolio, in close partnership with Finance.
- Lead firm\-wide AI ROI forecasting, financial benefits realisation, and quantified business case discipline across all pillars.
- Set financial targets, manage expense productivity, own the approval process for hiring and discretionary spend, and review monthly financials with the Group Head of AI to identify variances and corrective actions.
- Ensure efficient utilisation of AI infrastructure, compute, data, and vendor spend at global scale.
- Workforce Transformation \& Talent
- Partner with HR to lead the workforce transformation agenda for the firm \- including workforce impact modelling, reskilling, talent strategy, and organizational change required for an AI\-first Citi.
- Lead the Group AI workforce management council and associated deliverables; own headcount planning, location strategy, and talent mobility within the Group AI organization.
- Champion a culture of accountability, continuous improvement, and ownership across a global team operating in a regulated environment.
- Governance, Performance \& Reporting
- Act as Chief of Staff to the Group Head of AI: run executive governance forums, leadership offsites, and cadence with the Board, regulators, and the Operating Committee.
- Provide central oversight of business\-critical OKRs, milestone reporting, and executive dashboards to monitor the health of the AI portfolio.
- Establish and manage a robust governance framework for the tracking and realisation of strategic AI initiatives.
- Stakeholder Management
- Serve as the primary operating interface to the COO function, the Head of Tech \& Business Enablement, Finance, HR, Risk, Legal, and the business\-line COOs.
- Cultivate trusted, credible relationships with senior leaders across the firm; communicate progress, challenges, and successes to diverse audiences with tailored messaging.
What Success Looks Like (First 12 Months)
- A single, transparent AI portfolio view with clear ROI commitments by pillar and business line, agreed with Finance.
- A working operating rhythm (planning, governance, reporting) trusted by the Group Head of AI, the CFO, and the CHRO.
- A workforce transformation roadmap co\-owned with HR and adopted across the firm.
- Demonstrable acceleration in AI program delivery velocity \- without compromising controls.
Qualifications
- 15\+ years of progressive leadership experience in Financial Services, including a senior COO, Chief of Staff, or Business Execution role driving strategy, transformation, or large\-scale operational change in a regulated environment.
- Seasoned operator with proven success running Citi\-scale, highly\-matrixed transformation programs; able to set priorities and triage in a fast\-paced, ambiguous environment.
- Strong financial acumen \- credible partner to the CFO; demonstrated experience owning multi\-hundred\-million\-dollar investment portfolios, P\&L management, budgeting, and ROI realisation.
- Workforce and talent fluency \- credible partner to the CHRO; track record on workforce transformation, talent strategy, and change management at scale.
- Disciplined execution and operational excellence — process\-oriented, with experience applying structured methodologies to complex transformations.
- Technology and AI acumen \- fluency in modern AI sufficient to challenge, prioritise, and translate; track record of partnering on technology\-led transformation. Deep technical expertise is not required, but genuine curiosity and conviction are.
- Risk \& Compliance awareness \- comprehensive understanding of corporate governance, regulatory frameworks, and risk management in global banking.
- Influence \& communication \- excellent communication and interpersonal skills; able to influence at Board, regulator, and Operating Committee level.
- Low\-ego integrator \- the kind of leader whose presence quietly elevates every other executive in the room; bias to action; resilient under pressure.
- Education
- Bachelor's degree required, preferably in business, finance, or a related field.
- MBA or other advanced degree strongly preferred.
Why This Role
You will be the operating partner to the Group Head of AI at the moment Citi reorganizes itself around artificial intelligence. The decisions you make in the first year \- on operating model, on capital allocation, on workforce \- will define how the firm competes for the next decade
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Job Family Group:
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Business Strategy, Management \& Administration
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Job Family:
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Business Execution \& Administration
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Time Type:
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Full time
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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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$250,000\.00 \- $500,000\.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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Jul 22, 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 $250K-$500K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($375K) sits 71% above the category median. Disclosed range: $250K to $500K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Citi AI Hiring
Citi has 9 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, US, Jersey City, NJ, US, Tampa, FL, US. Compensation range: $160K - $500K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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