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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.
Citi is seeking a AI Risk \& Governance \- SVP to lead the responsible AI agenda for Trade \& Working Capital Solutions (TWCS) — one of the most strategically significant and globally scaled franchises within Citi's Institutional Clients Group, operating across 160\+ countries. In this role, you will establish the control frameworks, testing strategies, and explainability standards that govern Citi's AI deployment in trade finance, ensuring every AI capability is built responsibly, governed transparently, and maintained to the highest standards of regulatory readiness. This is a critical senior leadership position at the intersection of AI innovation and risk control — with direct influence on how AI shapes the future of global trade finance at institutional scale.
Responsibilities
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- Lead end\-to\-end AI risk identification, assessment, and management across all TWCS AI solutions, establishing and maintaining a consolidated AI risk register in coordination with Model Risk Management, Operational Risk, and Technology stakeholders.
- Define AI\-specific risk appetite thresholds, escalation protocols, and material risk reporting frameworks aligned to enterprise risk management standards, ensuring AI risk posture is continuously monitored and remediated within approved tolerance.
- Architect and own the comprehensive AI testing strategy for all TWCS machine learning solutions — spanning pre\-production validation through post\-deployment monitoring — with performance benchmarks, fairness metrics, and robustness protocols calibrated to trade finance use cases including document digitization, fraud detection, and counterparty risk scoring.
- Design, implement, and operationalize a fit\-for\-purpose AI Governance framework for the Trade \& Working Capital portfolio, covering model lifecycle policies, AI inventory management, and governance committee structures aligned to the EU AI Act, SR 11\-7, and OCC Model Risk guidance.
- Lead the design and integration of Explainable AI (XAI) capabilities — including SHAP, LIME, and counterfactual reasoning — ensuring model decisions are interpretable and defensible for technical, executive, client, and regulatory audiences alike.
- Ensure all TWCS AI solutions maintain perpetual audit readiness, building and maintaining comprehensive model documentation artefacts including Model Risk Cards, bias assessments, data lineage maps, and control attestations across Internal Audit, regulatory examination, and external review cycles.
- Manage the geographic expansion of AI governance controls across 30\+ markets, adapting frameworks to local regulatory requirements while maintaining global design consistency and control reusability across the TWCS AI estate.
Required qualifications \& skills
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- 12\+ experience in financial services risk, technology, or AI/ML governance — including direct ownership of enterprise\-level risk or governance programmes.
- Demonstrated experience building, launching, or governing AI/ML solutions within regulated financial services environments, with direct accountability for model risk management or AI governance frameworks.
- Deep knowledge of AI and ML concepts, responsible AI frameworks, model risk management, and explainability techniques including SHAP and LIME, combined with fluency in enterprise governance requirements such as SR 11\-7, the EU AI Act, and OCC Model Risk guidance.
- Demonstrated experience leading Internal Audit reviews and regulatory examinations with bodies including the FRB, OCC, PRA, and MAS — with a track record of driving audit findings to timely, validated closure.
- Exceptional stakeholder management capability — able to build consensus, influence across a matrixed global organization, and align risk and governance strategy from engineering teams through to senior risk committees and external regulators.
- Outstanding written and verbal communication skills — able to translate complex AI risk and governance concepts with equal clarity for executive, regulatory, technical, and client audiences.
- Ability to self\-direct, operate effectively in ambiguous environments, manage competing deadlines, and deliver high\-quality outcomes consistently in large\-scale, fast\-paced institutional settings.
Beneficial skills \& qualifications
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- Trade finance domain knowledge — including familiarity with Letters of Credit, Bank Guarantees, Supply Chain Finance, Documentary Collections, and associated regulatory frameworks.
- Familiarity with generative AI tools and large language model governance, and their practical application within trade finance operations and enterprise productivity workflows.
- Experience applying agile delivery methodologies within cross\-functional AI product and risk teams at enterprise scale.
- Advanced degree (MSc or MBA) in Artificial Intelligence, Data Science, Financial Engineering, Risk Management, or a related discipline.
- Relevant professional certifications such as GARP Model Risk Manager (GARP\-MRM), GARP Certificate in AI Risk \& Governance, Certified AI Governance Professional (CAIGP), CRISC, AI Audit Certificate (ISACA), EU AI Act Practitioner Certificate, or cloud AI/ML specializations (AWS, Azure, or GCP).
What we offer
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This role offers a rare opportunity to shape how AI is governed at the heart of one of the world's most globally connected financial institutions — with the strategic scope, senior visibility, and institutional backing to make a lasting impact on the future of trade finance. At Citi, you will work at pace, with purpose, and alongside teams that operate at the highest levels of global banking.
- Direct strategic influence over Citi's AI governance agenda across a global trade finance franchise spanning 160\+ countries.
- A hybrid working model with 3 days in the office and 2 days working remotely, providing flexibility alongside meaningful in\-person collaboration.
- Access to Citi's global learning and development resources, supporting continuous professional growth and leadership development at senior level.
- Wellbeing support programs designed to sustain performance and work\-life balance across all stages of your career.
- Competitive financial wellbeing benefits, including compensation structures, retirement planning, and long\-term financial support appropriate to a senior leadership role.
- Family support benefits and policies that recognize and accommodate the full range of personal commitments.
- The opportunity to engage with and influence global regulators, senior risk committees, and cross\-functional leadership as a recognized authority in AI governance.
Apply now to take ownership of AI governance at global scale and help define how responsible AI is built and deployed across one of the world's leading trade finance platforms.
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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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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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$176,720\.00 \- $265,080\.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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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 $176K-$265K 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. Disclosed range: $176K to $265K.
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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