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 AI Governance \& AI\-First Ways of Working to lead the governance, prioritisation, and adoption of AI across Citi's global Operations function. This is a senior leadership role with end\-to\-end accountability for managing the Operations AI use case pipeline — ensuring that investment is aligned to strategic priorities, that AI capabilities are adopted consistently, and that the function builds the organisational behaviours needed to operate in an AI\-first way.
This role is for a senior operations leader who understands that the hardest part of AI transformation is not the technology — it is the governance, the culture, and the discipline to ensure that the right things get built, adopted, and sustained. If you combine strategic thinking with practical delivery experience and a passion for embedding technology\-driven change across large, complex organisations, this role offers the mandate and platform to make a lasting impact.
Responsibilities
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- Own the Operations AI intake process from end to end — evaluating, prioritising, and governing AI use cases to ensure investment decisions are grounded in strategic objectives and measurable business value.
- Drive AI\-first ways of working across Operations, accelerating capability adoption, embedding new tooling into day\-to\-day practice, and leading the organisational change needed to shift how teams operate.
- Chair cross\-functional Operations forums, bringing together leaders from Product, Technology, Risk, Compliance, and Operations to reach well\-informed prioritisation and governance decisions.
- Partner with Product leads to align use case prioritisation across functions, ensuring Operations requirements are reflected in platform and capability planning.
- Define and track adoption metrics, engagement indicators, and business outcomes across AI capabilities, using data\-driven insights to continuously improve utilisation and value realisation.
- Support the transition of Operations towards AI\-first product development practices, guiding teams through tooling adoption and embedment as ways of working evolve.
- Lead multiple senior operations managers across large or distributed teams, setting clear performance expectations and making decisions on hiring, development, and organisational structure.
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 govern complex, cross\-functional programmes — with full accountability for outcomes, planning, budgets, and strategic direction across multiple teams.
- Clear track record of leading organisational change and capability adoption at scale, translating strategic priorities into operational practice across large teams.
- Strong commercial awareness and analytical capability, including the ability to evaluate AI use cases against risk, reward, and business value criteria to inform investment decisions.
- Ability to assess and manage risk in business decisions, maintaining consistent standards for compliance, controls, and sound ethical judgement that protect the firm and its clients.
- Excellent communication and negotiation skills, with experience influencing senior stakeholders across functions and building alignment around shared priorities.
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 a deep understanding of what it takes to govern AI adoption responsibly across high\-volume, compliance\-sensitive environments.
- AI/Automation Program Leaders: Senior leaders who have owned governance, prioritisation, and intake frameworks for AI and intelligent automation programmes within operations contexts — ensuring the right use cases get resourced and the right outcomes are tracked.
- Transformation Leaders: Individuals who have driven AI\-first culture change across large operations organisations — embedding new ways of working, accelerating tooling adoption, and sustaining behavioural change beyond the initial launch phase.
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 — and uses that empathy to make better governance decisions.
- 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 ensuring that AI tools are intuitive, reliable, and trusted by the people who depend on them daily. Understands that governance exists to protect and accelerate adoption — not to block it.
- 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.
- Governance with Measurable Impact: Has a track record of governing AI or automation programmes that produced measurable operational outcomes — reduced average handle time, improved first\-contact resolution, or lower cost\-per\-transaction — and can clearly demonstrate the link between governance decisions and business impact.
- Adoption at Scale: Can point to AI governance frameworks applied across large operations teams (hundreds to thousands of users), with evidence of sustained adoption, change management, and measurable value realisation rather than one\-off deployments.
- Cross\-Functional Operator in Regulated Environments: Experienced in governing technology initiatives in close partnership with Operations, Risk, Compliance, and Technology — navigating complex governance frameworks without losing delivery momentum.
- Process\-First Mindset: Demonstrates a consistent habit of ensuring the underlying process is understood and re\-engineered before investment is committed to automation — not simply governing the digitisation of broken workflows.
Beneficial Skills \& Qualifications
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- Prior experience within a specific line of business in financial services operations, providing relevant context for the scope and complexity of AI governance at this level.
- Familiarity with AI product development lifecycles, use case intake frameworks, or capability governance models within an enterprise or regulated environment.
- Experience working alongside Product and Technology functions to co\-define roadmaps and embed operational requirements into technology and AI delivery cycles.
What We Offer
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This role places you at the centre of one of Citi's most strategically important operational programmes, with genuine authority to govern how AI is adopted, prioritised, and embedded across a global function.
- A hybrid working model with 3 days in the office and 2 days working remotely, providing flexibility while keeping you connected to your teams and senior stakeholders.
- Senior\-level ownership of a high\-visibility governance and transformation agenda, with the mandate to shape how Citi's Operations function approaches AI adoption at a global scale.
- Access to Citi's global network and cross\-functional leadership community, enabling meaningful collaboration across operations, technology, data, and product disciplines.
- 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 current at the intersection of AI governance, operational transformation, and enterprise capability adoption.
- Wellbeing support and work\-life balance resources designed to sustain high performance across all dimensions of your career and personal life.
*Apply now to lead AI governance and cultural change across Citi's global Operations function, and help shape the way one of the world's leading financial institutions works.*
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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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