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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 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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Senior Vice President (SVP) – Head of AI/ML Solutions \& Client Intelligence
Citi is seeking an experienced and visionary technology leader to lead the development and delivery of next\-generation Artificial Intelligence (AI), Machine Learning (ML), and Advanced Analytics solutions supporting Institutional Sales, Investment Banking, Corporate Banking, Treasury \& Trade Solutions, Investor Services, Issuer Services, Prime Finance, and Investment Research.
This leader will drive the AI strategy and execution for Citi's Customer Relationship Management (CRM), Revenue Analytics, Client Profitability, Client Intelligence, and Relationship Management platforms. The role combines AI/ML leadership, data science, software engineering, business partnership, and people management to deliver measurable business outcomes that enhance client engagement, revenue growth, productivity, and profitability.
The successful candidate will lead a team of engineers and data scientists while partnering closely with Sales, Banking, Relationship Managers, Research Analysts, Product Managers, and Technology leaders across the organization.
Key Responsibilities
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### AI \& Data Science Leadership
- Define and execute the strategic roadmap for AI, Machine Learning, Generative AI, and advanced analytics capabilities across Citi's client and revenue management platforms.
- Build intelligent solutions that improve client acquisition, retention, wallet\-share growth, cross\-selling opportunities, and relationship effectiveness.
- Develop predictive models for:
+ Client revenue forecasting
+ Client profitability optimization
+ Client churn and retention analysis
+ Coverage optimization
+ Relationship intelligence
+ Next\-best\-action recommendations
+ Opportunity identification
- Implement Generative AI and Agentic AI solutions to enhance banker, salesperson, relationship manager, and research analyst productivity.
### Client Intelligence \& Analytics
- Design and deliver advanced client intelligence solutions that leverage:
+ CRM interaction data
+ Call reports
+ Client feedback surveys
+ Research consumption data
+ Market and pricing data
+ Transaction and revenue data
+ External and alternative data sources
- Develop business intelligence capabilities that provide a comprehensive view of client relationships, engagement patterns, and growth opportunities.
- Build advanced recommendation engines and knowledge discovery capabilities that surface actionable insights to front\-office teams.
### Technology Leadership
- Lead end\-to\-end delivery of AI\-powered applications from ideation through production.
- Establish scalable AI/ML platforms, MLOps practices, and model governance frameworks.
- Drive architecture decisions across:
+ Data engineering
+ Data integration
+ Machine learning platforms
+ Cloud and distributed computing
+ Real\-time analytics
+ API and microservices architectures
- Ensure solutions meet Citi's standards for security, compliance, model governance, resilience, and operational excellence.
### Forward\-Deployed Engineering \& Business Partnership
- Function as a strategic technology advisor to business stakeholders.
- Collaborate directly with senior leaders across Sales, Banking, Research, Treasury, Investor Services, Issuer Services, and Prime Finance.
- Translate business challenges into data\-driven solutions and measurable outcomes.
- Drive rapid experimentation and deployment of AI capabilities aligned with strategic business priorities.
### Team Leadership
- Lead, mentor, and develop a high\-performing team of approximately 6 engineers and data scientists.
- Foster a culture of innovation, accountability, continuous learning, and engineering excellence.
- Build organizational AI capabilities and promote adoption of emerging technologies across the business.
Qualifications
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### Required Experience
- 8\+ years of technology experience, including:
+ AI/ML and Data Science
+ Enterprise Application Development
+ Data Engineering
+ Analytics Platforms
+ Financial Services Technology
- 5\+ years leading engineering and/or data science teams.
- Demonstrated experience delivering AI solutions in production environments.
- Experience partnering directly with business stakeholders and executive leadership.
### Technical Skills
AI / Machine Learning
- Machine Learning, Deep Learning, NLP, LLMs, Generative AI
- Predictive Analytics
- Recommendation Systems
- Knowledge Graphs
- Retrieval\-Augmented Generation (RAG)
- Agentic AI Architectures
- Model Evaluation and Governance
Programming \& Engineering
- Python
- Java and/or .NET
- SQL
- Spark
- Distributed Data Processing
- REST APIs
- Microservices Architecture
Data \& Analytics
- Data Modeling
- Data Warehousing
- Data Integration
- ETL/ELT Frameworks
- Streaming Technologies
- Data Governance
- Master Data Management
- Business Intelligence Platforms
DevOps \& MLOps
- CI/CD Pipelines
- GitHub
- Kubernetes
- Docker
- Infrastructure as Code
- Model Monitoring
- Feature Stores
- MLOps Frameworks
### Domain Expertise
Strong understanding of one or more of:
- Institutional Sales
- Investment Banking
- Corporate Banking
- Treasury \& Trade Services
- Investor Services
- Issuer Services
- Prime Finance
- Capital Markets
- Investment Research
- Relationship Management
- Client Coverage Models
Leadership Competencies
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- Strategic Thinker
- Technology Visionary
- Business Outcome Focused
- Strong Executive Presence
- Data\-Driven Decision Maker
- Exceptional Communication Skills
- Talent Developer and Coach
- Collaborative Partner Across Business and Technology
Success Measures
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Within the first 12–24 months, this leader will:
- Establish Citi's AI roadmap for client intelligence and relationship management platforms.
- Deliver AI\-powered client intelligence and revenue growth capabilities.
- Improve banker and salesperson productivity through intelligent workflow automation.
- Enhance client profitability and coverage effectiveness through predictive analytics.
- Operationalize Generative AI solutions within CRM and client engagement platforms.
- Build a scalable AI engineering and MLOps foundation supporting enterprise growth.
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Job Family Group:
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Technology
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Job Family:
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Applications Development
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Time Type:
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Full time
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Primary Location:
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Jersey City New Jersey 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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Aug 16, 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 $176K-$265K 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 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 $218,750 based on 3,817 positions with disclosed compensation. Disclosed range: $176K to $265K.
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
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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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