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About This Role
Discover your future at Citi
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
Citi is looking for a Senior Manager, AI Risk and Controls to lead independent oversight of AI use cases and AI assets across the business, ensuring every deployment meets Citi's governance standards and applicable regulatory requirements. Sitting within Citi's Non-Financial Risk function, this is a high-impact leadership role that places you at the centre of one of the most strategically important challenges in financial services — governing AI responsibly at scale. You will operate with delegated authority, shape AI risk appetite, and serve as the senior escalation point for complex AI risk matters across a global portfolio.
Responsibilities
- Exercise delegated approval authority for AI use cases and AI assets, conducting comprehensive risk and controls assessments and providing the required first-line sign-off for higher-risk deployments before escalation to the second line of defence or governance committees.
- Lead independent, risk-based reviews of pre-release testing results, control design, residual risk, and approval conclusions — including assessment of guardrail adequacy and mitigating strategies — to confirm all requirements are met before any AI asset moves into production.
- Monitor AI use case and asset performance against approved parameters, challenge risk indicators and control effectiveness, and escalate material control failures, performance breaches, or unapproved activity promptly to the appropriate governance and risk teams.
- Assess the impact of new and changed AI use cases on existing business processes and controls, ensuring material changes in scope, technology, data, autonomy, or regulatory profile are evaluated and routed for reapproval before implementation.
- Build, lead, and develop a high-performing team of AI risk and control professionals, setting clear objectives, providing mentorship, and establishing a culture of accountability and continuous improvement.
- Represent the AI risk and control function at senior executive forums, governance committees, and Board-level discussions, serving as the authoritative voice on AI risk posture and control adequacy across the portfolio.
- Define and drive the strategic multi-year roadmap for the AI risk and control framework, ensuring alignment with Citi's enterprise AI strategy and the evolving regulatory landscape including the EU AI Act and NIST AI Risk Management Framework.
Required qualifications & skills
- 15 or more years of experience in risk management, controls, or a related field within a large-scale, global financial institution, including 8 or more years in a senior management capacity across multiple disciplines.
- Established track record leading and managing senior teams (VP or Director level) within a matrixed, global organisation, including experience defining operating models and building risk and control functions from the ground up.
- Deep knowledge of AI and machine learning models and their associated risks — particularly for Generative AI and Agentic AI — including controls to manage hallucination, model drift, bias, and autonomy risks across the full deployment lifecycle.
- Ability to assess and classify AI risk levels across development, pre-production testing, production monitoring, and decommissioning stages, and to credibly challenge testing results, model performance metrics, drift detection outputs, fairness metrics, and explainability reports.
- Broad knowledge of AI governance frameworks, risk management policies, applicable AI regulation (including the EU AI Act and NIST AI RMF), and associated risk disciplines including Model Risk, Cyber Risk, Third Party Risk, and Data Governance.
- Exceptional ability to influence senior stakeholders across business, technology, compliance, and operations functions, and to communicate complex AI risk concepts clearly to both technical and non-technical audiences.
- Bachelor's degree or equivalent university qualification in a relevant field.
Beneficial skills & qualifications
- Master's degree in a relevant discipline such as risk management, data science, finance, or law.
- Experience building and scaling a risk and control function from inception, including defining the organisational structure, talent strategy, and resource model.
- Familiarity with AI fairness metrics, explainability frameworks, and adversarial risk evaluation methodologies within a regulated financial services environment.
What we offer
This role offers the opportunity to shape how one of the world's leading financial institutions governs AI at the most senior level — with real authority, global reach, and direct influence on strategic decision-making. You will work alongside leaders across technology, compliance, enterprise risk, and the business, and be positioned to make a measurable impact on Citi's AI risk posture for years to come.
- A hybrid working model with 3 days in the office and 2 days working remotely, giving you flexibility alongside meaningful in-person collaboration.
- Senior leadership visibility, including representation at Board-level governance forums and cross-functional executive committees.
- Strategic ownership of a high-profile function at a pivotal moment in the evolution of AI governance in financial services.
- Access to Citi's global network and the breadth of resources that come with working at one of the world's largest financial institutions.
- Ongoing learning and professional development opportunities, including engagement with regulatory developments, industry frameworks, and emerging AI risk practices.
- Competitive financial wellbeing and benefits package commensurate with a senior leadership position.
Apply now to take a defining leadership role in AI risk governance at Citi — where your expertise will shape the safe and responsible use of AI across a global portfolio.
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Job Family Group:
Controls Governance & Oversight -
Job Family:
Cross-disciplinary Controls -
Time Type:
Full time -
Primary Location:
Getzville New York United States -
Primary Location Full Time Salary Range:
$200,000.00 - $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
Please see the requirements listed above. -
Other Relevant Skills
Analytical Thinking, Assessment, Business Insights, Constructive Debate, Controls Lifecycle, Controls Management, Data Quality Management, Digital Skills (Including AI), Risk Management Lifecycle, Stakeholder Management. -
Anticipated Posting Close Date:
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Automated Processing and AI
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 $200K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $200K to $300K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Citi AI Hiring
Citi has 69 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span Bengaluru, Karnataka, India, Getzville, NY, US, Mumbai, Maharashtra, India. Compensation range: $160K - $300K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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