Interested in this AI/ML Engineer role at Citi?
Apply Now →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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*At Citi, you won't be running a typical IT implementation. You'll be at the center of redefining how one of the world's largest financial institutions actually operates — from the inside out. Services Operations touches millions of client interactions, thousands of associates, and some of the most complex workflows. You'll have the mandate, the access, and the executive backing to deploy AI where it matters most: at the point where our people serve our clients. The problems are real, the scale is unmatched, and the impact is immediate and measurable.*
Citi is looking for a Director, Operations Horizontal AI Solutions to lead the design and delivery of reusable AI capabilities that can be deployed consistently across multiple operations functions globally. Sitting at a senior level within Citi's Operations function, this role is central to reducing duplication, accelerating AI adoption, and ensuring that common operational challenges are solved once — then scaled across the firm.
This is a role for a seasoned operational leader who understands that the biggest gains in AI transformation come not from solving the same problem twenty times over, but from building once and deploying everywhere. If you have a track record of building shared capabilities and driving cross\-functional AI programmes at scale, this is an opportunity to define how Citi operationalises AI across its global operations estate.
Objective
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Implement reusable AI capabilities across Operations that can be deployed across multiple operations functions — addressing common processes and eliminating duplication at scale.
Top 5 Responsibilities
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Drive standardisation, reuse and common solutions across Operations to reduce duplication and accelerate AI adoption across global teams. Partner with Product, Technology, Data, and Control functions to ensure scalable, secure and compliant execution in Operations — ensuring Ops requirements are incorporated into platform roadmaps and the execution of supporting operational readiness, training, and testing activities. Define and support the strategy and roadmap for horizontal AI capabilities that can be leveraged across Operations and export best of breed from enterprise Operations. Develop reusable capability patterns — including knowledge assistants, operational intelligence, workflow copilots and decision support services. Manage Operations solutions portfolio — ensuring adoption, operational impact and strategic alignment.
Full Responsibilities
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- Drive standardisation and reuse of AI solutions across global Operations teams, eliminating duplication and accelerating the adoption of common capabilities at scale.
- Define and continuously evolve the strategy and roadmap for horizontal AI capabilities — ensuring they can be leveraged across multiple operations functions and represent best\-in\-class execution within the enterprise.
- Develop reusable capability patterns including knowledge assistants, operational intelligence tools, workflow copilots, and decision support services that address common operational processes.
- Partner with Product, Technology, Data, and Control functions to ensure AI solutions are scalable, secure, and compliant — and that Operations requirements are embedded into platform roadmaps from the outset.
- Lead the execution of operational readiness, training, and testing activities that support the successful deployment of AI capabilities across Operations teams.
- Manage the Operations AI solutions portfolio, tracking adoption, measuring operational impact, and maintaining alignment with strategic priorities across the function.
- Influence senior stakeholders across functions to build consensus around shared approaches, and negotiate effectively to ensure horizontal solutions are adopted rather than replicated locally.
Required Qualifications \& Skills
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- 15 or more years of experience in operations or a related function, including at least 8 to 10 years leading large, matrixed teams through other managers.
- Demonstrated ability to design and deliver shared or horizontal capability programmes across complex, cross\-functional environments, with full accountability for outcomes and strategic direction.
- Deep knowledge of operational process design and the practical application of AI, automation, or shared services models to improve end\-to\-end service delivery at scale.
- Commercial acumen and analytical capability, including a working understanding of how technology investments translate into operational efficiency, risk reduction, and sustainable performance.
- Ability to assess risk in operational and business decisions, maintaining consistent standards for controls, compliance, and ethical conduct that protect the firm and its clients.
- Excellent communication and negotiation skills, with a track record of influencing senior stakeholders across multiple functions and aligning diverse teams around common solutions.
Profile
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We are looking for candidates who fit one or more of the following profiles:
- Operations or Operations Technology Leaders: Professionals who have led technology\-enabled transformation within operations functions at large regulated institutions — with direct experience of high\-volume, compliance\-sensitive service environments and the realities of building for operators at scale.
- AI/Automation Program Leaders: Senior leaders who have implemented AI and intelligent automation specifically within operations contexts, solving real workflow problems through reusable, scalable capability rather than one\-off solutions.
- Transformation Leaders: Individuals who have deployed and scaled agentic AI solutions, implemented intelligent routing, and delivered workflow copilots and agent\-assist tools that are trusted and adopted by large operations teams globally.
Key Attributes
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- Operational Empathy: Has walked in the shoes of an operations team. Understands the day\-to\-day realities of SLAs, case management, escalation paths, and the human impact of getting automation wrong.
- Pragmatic Technologist: Possesses strong working knowledge of AI — including LLMs and agentic frameworks — but filters every technical decision through the lens of operational feasibility, auditability, and risk. Can credibly partner with both product/engineering teams and operations leaders.
- Builds for Operators: Obsessed with implementing tools that are intuitive, reliable, and trusted by the people who depend on them daily. Understands that horizontal adoption requires solutions that feel built for the operator, not just technically sound.
- Change Agent with Discipline: Comfortable navigating the ambiguity of emerging AI capabilities, while remaining grounded in the governance and control requirements of a regulated financial institution. Brings structure to transformation without stifling momentum.
- Proven Delivery at Scale Across Multiple Functions: Has a track record of deploying AI or automation solutions that produced measurable operational outcomes — reduced average handle time, improved first\-contact resolution, or lower cost\-per\-transaction — across multiple functions, not just pilot programmes, with evidence of sustained adoption and change management.
- Cross\-Functional Operator in Regulated Environments: Experienced in delivering technology initiatives in close partnership with Operations, Risk, Compliance, and Technology — navigating governance frameworks without losing delivery momentum.
- Process\-First Mindset: Demonstrates a consistent habit of understanding and re\-engineering the underlying process before layering in automation — not simply digitising broken workflows. Understands that horizontal solutions only scale if the process underneath is sound.
Beneficial Skills \& Qualifications
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- Prior experience building or governing shared capability platforms, centres of excellence, or common services functions within a large financial services or regulated environment.
- Familiarity with AI capability patterns such as knowledge assistants, copilot tools, operational intelligence, or decision support services in an enterprise context.
- Experience partnering with Product and Technology functions to influence platform roadmaps and embed operational requirements into technology delivery cycles.
What We Offer
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This role offers genuine strategic ownership within one of Citi's most forward\-looking operational programmes — with the mandate, seniority, and cross\-functional reach to shape how AI capabilities are built, shared, and scaled across a global institution.
- A hybrid working model with 3 days in the office and 2 days working remotely, giving you flexibility without sacrificing connection to your teams and stakeholders.
- Senior\-level ownership of a high\-visibility programme, with the authority to set direction, build shared capability, and drive adoption across global operations teams.
- Access to Citi's global network and cross\-functional leadership community, enabling collaboration across operations, technology, data, and product at an institution of significant scale.
- Competitive compensation and financial wellbeing benefits commensurate with a Director\-level position at a leading global financial institution.
- Ongoing learning and professional development opportunities to stay at the forefront of enterprise AI adoption and large\-scale operational transformation.
- Wellbeing support and work\-life balance resources designed to help you perform at your best across all dimensions of your career and personal life.
*Apply now to take strategic ownership of Citi's horizontal AI capability agenda and deliver solutions that scale across a global operations function.*
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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 Process Re\-Engineering
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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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$170,000\.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 21, 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 $170K-$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 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 $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 ($235K) sits 9% above the category median. Disclosed range: $170K 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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