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About This Role
Individuals in Issue Management are responsible for the coordination and comprehensive management of issues with key stakeholders, such that Citi achieves and maintains compliance and appropriately remediates findings from internal and external reviews as well as self-identified issues. Includes coordinating with key stakeholders to investigate controls gaps or failures, develop corrective action plans, and provide robust challenge enabling the key stakeholders to implement sustainable solutions by addressing root causes and adopting enhanced discipline including consideration of lessons learned for the timely closure of issues.
As the Issue Management team advances its use of AI and large language models within the Issue Management Lifecycle, dedicated business‑side expertise is essential to connect technical model development with governance, risk controls, and operational readiness. This role ensures that AI solutions are designed, documented, evaluated, and implemented in a responsible and compliant manner.
Responsibilities:
- Assist in the coordination and comprehensive management of issues with key stakeholders
- Support issue quality reviews ensuring compliance with Issue Management Policy, Standards and Procedures
- Facilitate Business / Function Quality Control engagement across the full lifecycle of high impact issue remediations and key remediation programs
- Maintain a robust tracking and reporting issue inventory including issue quality metrics to provide visibility on the status of control gaps and Issues quality control processes highlighting risk and escalating concerns in timely manner
- Provide challenge enabling the key stakeholders to implement sustainable solutions to address root causes
- Utilize analytics to assess issue remediation trends, identify key risks, and develop insights for process improvement
- Support identification and analysis of potential control gaps and operational risks across Citi, leveraging data analysis to assess impact.
- Support key stakeholders to develop corrective action plans to address identified control gaps or failures
- Incorporate lessons learned guidance into the development of corrective action plans, promoting continuous improvement and timely closure of issues
- Ensure compliance with all relevant regulatory requirements and internal policies
- Partner with internal audit, business units, and senior management, communicating effectively to drive issue resolution, influence decision-making, and promote a culture of risk awareness
- Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency, as well as effectively supervise the activity of teams and create accountability with those who fail to maintain these standards
Qualifications:
- Minimum of 6-10 years of experience in operational risk management, compliance, audit, or other control-related functions in the financial services industry.
- Ability to identify, measure, and manage key risks and controls.
- Strong knowledge in the development and execution for controls.
- Proven experience in control related functions in the financial industry.
- Proven experience in implementing sustainable solutions and improving processes.
- Understanding of compliance laws, rules, regulations, and best practices.
- Understanding of Citi’s Policies, Standards, and Procedures.
- Strong analytical skills to evaluate complex risk and control activities and processes.
- Strong verbal and written communication skills, with a demonstrated ability to engage at the senior management level.
- Strong problem-solving and decision-making skills
- Ability to manage multiple tasks and priorities.
- Proficiency in Microsoft Office suite, particularly Excel, PowerPoint, and Word.
Education:
Bachelor's/University degree, Master's degree preferred
Additional Job Description:
- Serve as the business lead for applying industry‑standard model governance practices to AI and machine learning solutions.
- Lead the development and maintenance of technical and risk documentation, including model overviews, assumptions, limitations, risk controls, and testing evidence.
- Translate complex AI/ML model concepts into clear, structured business documentation suitable for risk, compliance, and audit review.
- Partner with data science and engineering teams to understand model behavior and testing results.
- Review technical test outputs related to model performance, bias detection, drift analysis, and robustness.
- Identify potential risks and ensure appropriate mitigants, controls, and monitoring plans are in place.
- Act as the primary business interface for AI model‑related conversations involving technology, risk, compliance, and operations teams.
- Provide guidance to project teams on required documentation, checkpoints, and evidence needed to progress through the AI development and deployment lifecycle.
- Support lifecycle activities including model onboarding, updates, annual reviews, and monitoring.
- Stay current on emerging AI risks, regulatory developments, model validation practices, and responsible AI principles.
- Provide business partners with guidance on AI governance best practices, model risk considerations, and documentation requirements.
Preferred Experience:
- Strong exposure to model governance, model documentation, model validation, or quantitative risk concepts (AI/ML preferred).
- Familiarity with large language models, generative AI workflow components, and how these differ from traditional statistical/ML models.
- Ability to interpret technical model details and translate them into business‑friendly documentation.
- Experience working with cross‑functional stakeholders across tech, business, and risk groups and navigating complex environments.
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Job Family Group:
Controls Governance & Oversight
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Job Family:
Issue Management
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Time Type:
Full time
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Primary Location:
Tampa Florida United States
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Primary Location Full Time Salary Range:
$103,920.00 - $155,880.00
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
Analytical Thinking, Communication, Constructive Debate, Controls Lifecycle, Issue Management, Management Reporting, Policy and Procedure, Risk Management, Root Cause Analysis.
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Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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Anticipated Posting Close Date:
Feb 04, 2026
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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.*
*If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review* *Accessibility at Citi**.*
*View Citi’s* *EEO Policy Statement* *and the* *Know Your Rights* *poster.*
Salary Context
This $103K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 1414 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $210,000 based on 1,345 positions with disclosed compensation. This role's midpoint ($129K) sits 38% below the category median. Disclosed range: $103K to $155K.
Across all AI roles, the market median is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. For comparison, the highest-paying categories include Research Scientist ($260,000) and AI Architect ($251,680). By seniority level: Entry: $125,000; Mid: $202,000; Senior: $240,000; Director: $255,600; VP: $225,000.
Citi AI Hiring
Citi has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span Tampa, FL, US, New York, NY, US, Boca Raton, FL, US. Compensation range: $155K - $245K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: New York (228 roles, $223,400 median); San Francisco (216 roles, $255,750 median); Los Angeles (172 roles, $204,300 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $220,000. Top-quartile compensation starts at $260,000. The 90th percentile reaches $311,800. Highest-paying categories: Research Scientist ($260,000 median, 48 roles); AI Architect ($251,680 median, 9 roles); Research Engineer ($250,200 median, 8 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $220,000. Top-quartile roles start at $260,000, and the 90th percentile reaches $311,800. 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. Research Scientist roles lead at $260,000 median, while AI/ML Engineer roles sit at $210,000. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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