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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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Citi's COO Technology organization is building one of the most consequential AI platforms in global financial services — and this is the role that leads it.
The Head of AI Solutions is a newly created, executive\-level position with a clear mandate: architect a unified AI strategy, build a world\-class engineering team, and deliver production\-grade AI at scale across the operational nerve center of a global bank. This is not a coordination or advisory role. It is a builder's role — one with the budget, the mandate, and the organizational reach to make it real.
You will own the AI strategy and delivery capability across a $200M\+ technology portfolio spanning some of the most operationally complex domains in banking: KYC, fraud detection, wholesale lending operations, global reconciliations, cash management, payments control, non\-financial regulatory reporting, payroll, and international operations. The scale is significant, the problems are unsolved at this level of complexity, and the impact is direct — the solutions you build will influence how trillions of dollars in transactions flow daily, how regulatory risk is managed, and how Citi's operational infrastructure evolves over the next decade.
Unlike a role at a pure\-play technology company, you will be solving AI challenges where failure has regulatory and systemic consequence — and where success reshapes the economics and resilience of critical global operations. The ambiguity is real, the stakes are high, and the opportunity for lasting impact is unmatched.
This role reports directly to the Head of COO Technology.
Responsibilities:
AI Strategy \& Platform Architecture:
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- Define and own the multi\-year AI strategy for the COO Technology portfolio; translate business intent into a concrete, milestone\-driven execution roadmap with measurable outcomes
- Develop architecture blueprints and end\-to\-end systems design for Generative AI and agentic workflows across diverse operational domains
- Build the shared AI platform — reusable models, tooling, guardrails, evaluation frameworks, and accelerators — that reduces duplication, lowers cost, and enables faster adoption across COO
- Establish and enforce engineering standards, architectural guardrails, and development patterns across all AI initiatives
- Define a rigorous total cost of ownership model for developing, deploying, and sustaining AI in production
- Identify and evaluate emerging GenAI technologies, foundation models, and agent frameworks — and make deliberate, defensible decisions on where to build, buy, or partner
Production AI Delivery at Enterprise Scale
- Lead end\-to\-end delivery of AI solutions across high\-complexity, regulated operational environments — from architecture through production deployment, monitoring, and continuous improvement
- Drive the agentic product development lifecycle: runtime harness design, evaluation frameworks, human\-in\-the\-loop workflows, feedback loops, and production readiness criteria
- Manage cross\-functional delivery spanning engineering, product, data, architecture, cyber, risk \& compliance, and operations
- Proactively identify and resolve dependencies, critical path risks, and systemic delivery blockers; ensure on\-time, on\-budget execution
- Ensure all AI solutions meet production\-grade standards: stability, scalability, auditability, explainability, and regulatory compliance
Executive Partnership \& AI Governance
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- Serve as the senior AI executive point of contact for COO function leads — partnering directly with the Heads of Core Operations Technology, Shared Services Technology, and Controls Technology
- Lead AI governance forums and Architecture Review Boards; establish clear decision rights and review cadences across the portfolio
- Translate complex technical realities into clear, compelling narratives for senior non\-technical audiences — including COO, CIO, and regulatory stakeholders
- Develop executive\-level communications — steering committee materials, portfolio dashboards, and milestone tracking — that improve decision velocity and reduce execution risk
- Ensure full adherence to Citi's internal policies, risk and control frameworks, model risk management (MRM) standards, and applicable regulatory requirements
Building the AI Engineering Organization
- Build, structure, and lead a high\-performing AI engineering function aligned to COO's operational priorities — including team topology, operating model, and career pathways
- Foster a culture of technical excellence, intellectual curiosity, and pragmatic innovation: engineers who ship production solutions, not prototypes
- Own and manage the AI technology portfolio budget (\~$200M), driving disciplined funding allocation, financial transparency, and cost\-to\-serve accountability
- Lead productivity and efficiency programs with measurable targets across cycle time, quality, throughput, and engineering leverage
- Define and optimize vendor and partner strategy, including strategic AI platform partnerships (e.g., Google, Anthropic), third\-party tooling, and outsourced delivery models
Qualifications:
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15\+ years of experience in Technology \- Required:
- Generative AI \& LLM Engineering: Deep, hands\-on expertise in large language models including model selection, fine\-tuning, prompt engineering, retrieval\-augmented generation (RAG), vector database design, and evaluation methodologies. You understand how models behave in production, not just in demos.
- Agentic Systems Design:Proven experience designing and deploying multi\-agent architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool\-use patterns, human\-in\-the\-loop workflows, and agentic safety at enterprise scale
- AI/ML Engineering \& MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights \& Biases, Arize), and lifecycle management using platforms such as MLflow, Vertex AI, or SageMaker
- Cloud AI Platforms: Demonstrated deployment of AI workloads on AWS, GCP, and/or Azure including managed ML services, scalable inference infrastructure, and vector stores
- Programming \& Frameworks: Strong Python proficiency; working knowledge of PyTorch or TensorFlow; applied experience with AI application frameworks (LangChain, LlamaIndex, or equivalents)
- Enterprise AI Architecture: Designing AI systems for regulated production environments: data security, model explainability, audit logging, access controls, and integration with legacy systems
Leadership \& Delivery \- Required:
- 15\+ years in technology, with a proven record of leading large\-scale engineering organizations through build\-out and transformation
- 10\+ years of management experience, including direct leadership of senior engineers and architects, and management of managers across global teams
- Demonstrated delivery of enterprise AI solutions with realized measurable business outcomes — not just successful pilots or proofs of concept
- Experience managing large, complex technology budgets ($50M\+) with accountability for financial transparency and ROI
- Track record of operating effectively in matrixed, cross\-functional organizations at the intersection of technology and operations
Domain \& Contextual Knowledge \- Strongly Preferred
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- Deep familiarity with financial services operations and the regulatory landscape — particularly KYC/AML, fraud, reconciliations, and regulatory reporting
- Strong understanding of model risk management (MRM) and what it takes to move AI from development to production under regulatory scrutiny
- Experience engaging with strategic technology partners (cloud providers, AI platform companies) at an executive level
Leadership Profile
- You build platforms, not point solutions — you instinctively seek the reusable, the shared, the scalable
- You are equally credible in a deep technical architecture review and a board\-level strategy discussion
- You attract, develop, and retain strong technical talent — engineers want to work for you because they grow
- You operate with clarity and urgency in ambiguous environments; complexity energizes rather than paralyzes you
- You communicate with precision — you can make a complex AI architecture concept land with a CRO, a COO, and a principal engineer, and you do it differently for each
Education:
- Bachelors required; Master's in CS, AI/ML, or related field preferred
What success looks like:
In the first year, we expect the successful candidate to:
- Establish the AI engineering team and operating model — hire and structure a high\-performing team with clear roles, responsibilities, and a strong culture
- Deliver 3\+ production\-grade agentic AI systems across priority COO domains, with measurable operational impact (cost, speed, quality, or risk reduction)
- Launch the shared AI platform — reusable RAG infrastructure, evaluation frameworks, and common tooling adopted across COO Technology
- Define and align the multi\-year AI roadmap with COO function leads and the Head of COO Technology, with clear prioritization, milestones, and funding allocation
- Establish AI governance — Architecture Review Boards, model risk processes, and compliance frameworks embedded in the delivery lifecycle
Why this role
- Scale that is rare. You will build AI capabilities across one of the world's most operationally complex banking platforms — with direct, measurable impact on how trillions of dollars in transactions are processed, controlled, and reported daily.
- Greenfield mandate. The team, the platform, and the strategy are yours to define. You will set the architectural direction, the engineering culture, and the standards that govern AI across the COO portfolio.
- Uniquely hard problems. Banking operations at this scale generate AI challenges that simply do not exist elsewhere — legacy system integration, regulatory auditability requirements, multi\-jurisdictional data constraints, and the need for explainability in consequential decisions. If you want to solve problems that matter and that are genuinely difficult, this is the role.
- Executive visibility and sponsorship. This role has CIO and COO\-level visibility, a clear organizational mandate, and the budget to execute without delay.
- Strategic partnerships. Collaborate directly with Google, Anthropic, and leading cloud AI providers to design and deploy core platform capabilities at scale.
*Citi is an equal opportunity and affirmative action employer. Qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.*
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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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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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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 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. Mid-level AI roles across all categories have a median of $200,000. 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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