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About This Role
Citi’s Commercial Bank (CCB) for the North American (NAM) region is a rapidly growing business, targeting emerging, mid and larger-sized companies based in the United States or Canada, with annual revenue generally from $20 million to $1 billion plus. To facilitate the CCB’s wholesale lending activities, Commercial Lending Management (CLM) serves as an in-business credit function organized around our strategic industries and global target market approach. CLM plays a critical role of the client delivery model for the CCB, translating thought-leadership, industry and product expertise, and an end-to-end ownership of the credit relationship into best-in-class lending solutions. We are seeking a dynamic and motivated credit professional to join the Consumer Products and Retail team.
The CLM Credit Officer facilitates the end-to-end lending process for a covered industry vertical through a team of credit professionals. CLM is essential for the delivery of a wide range of financial solutions to Citi CCB clients, working in direct partnership with Relationship Managers and Independent Risk, to execute on the firm’s and client’s business objectives. This can include working capital solutions, term loans and M&A financing, treasury and liquidity management services, foreign exchange, trade finance and interest rate derivatives. The primary responsibilities of CLM include credit underwriting, due diligence, structuring and documentation, and portfolio monitoring. As part of the business and in direct partnership with relationship coverage bankers, CLM teams provide thorough, objective analysis of the financial condition and credit worthiness of borrowers, including prevailing covered industry sector and product/market conditions, as well as the appropriate credit structure based on various risk considerations.
The CLM Credit Officer is responsible for approving and managing a material amount of credit risk associated with capital deployed across a variety of lending products appropriate for commercial banking relationships. The CLM Credit Officer is a critical partner in the pre-screening of pipeline opportunities with Relationship Management for assessing credit appetite and identifying key issues, providing structuring input on financing options, driving internal analytical work product and discussions on credit approvals, and maintain ongoing credit oversight of the lending portfolio relationships through quarterly and/or annual reviews along with covenant and other reporting monitoring, as applicable. The assigned industry/specialty vertical requires extensive knowledge of the unique sector/product drivers and key performance indicators to make well-informed credit underwriting decisions and proactively manage the portfolio based on a dynamic environment. The CLM Credit Officer provides sector and sub-segment insights for key internal and external stakeholders, contributes to industry-specific risk and portfolio content, including industry-level portfolio reviews, and leads the credit-specific risk assessments on new originations and portfolio requests.
The CLM Credit Officer role also requires an extensive understanding of various Citi and CCB-specific credit policies, processes, and procedures, and how to apply these concepts consistently in practice, including ensuring the portfolio is appropriately risk-rated and classified, properly secured (if applicable) and that early warning signs are established and proactively addressed. CLM represents a critical “first line of defense” control function for the bank, ensuring the CCB's credit process is conducted in accordance with all internal and regulatory frameworks, and is responsive to relevant inquiries. The CLM Credit Officer plays an important role in meeting a high standard for owning the work product of their respective portfolio and represent their covered accounts in numerous highly visible forums. A Credit Officer can advise or lead strategic projects for US or Global CLM initiatives and provide critical inputs that direct the future of credit formation within the CCB. Additionally, the CLM Credit Officer identifies, creates and helps to deliver the necessary technical skills training to various teams across CCB to promote the development and application of credit skills, mentoring/managing junior and other support staff.
Responsibilities:
- Facilitate the end-to-end lending process within the US CCB for the assigned relationships and portfolio
- Knowledgeable of assigned portfolio and industry, and able to articulate and support a credit view based on well-supported analysis that effectively balances risk and business objectives
- Ensure appropriate regulatory classification and minimizing net credit losses for covered portfolio, as well as identify emerging areas of concerns that should be escalated
- Provide accountability for decisioning using credit covering approval authority in tandem with Independent Risk
- Directly manage the analysis and workflow of underwriting requests for new-to-bank and existing credit relationships, including pre-screening opportunities, facilitate amendments and extensions, perform annual reviews and other credit requests, including the related diligence
- Lead the timely analysis and preparation of monthly and/or quarterly financial memo reviews of existing borrowers to track compliance with loan covenants, raising potential concerns and taking necessary actions in partnership with other key stakeholders for the assigned portfolio
- Direct and supervise the loan documentation process for both new transactions and amendments or modifications; including coordination with internal partners and/or outside legal counsel to ensure that all credit approval terms and conditions are appropriately represented in the closing documents
- Conduct periodic client calls on all portfolio clients and select new to bank opportunities in partnership with the Relationship Manager
- Develop and maintain an extensive knowledge in the various credit products and services offered to CCB clients
- Mentor and develop junior staff to become effective resources and future Credit Officers and Bankers
- Recommend changes to improve existing processes to achieve greater efficiency and controls
- Work proactively, and in a constructive and diplomatic manner, with internal and external stakeholders to keep transactions moving forward and in accordance with appropriate controls
Qualifications:
- Experienced credit underwriting executive with a deep and relevant Retail and Consumer Product industry expertise, particularly in commercial and cross-border lending
- 10+ years of experience in credit underwriting, portfolio and/ or risk management with a large multinational financial services organization
- Demonstrated knowledge of intermediate accounting theory and its practical application in the credit underwriting process
- Excellent communication (written, verbal, presentation and listening) and organizational skills; ability to complete multiple priorities in a timely manner
- Strong credit sense and ability to articulate a view, work across a range of credit products, with experience in early problem recognition and resolution skills
- Demonstrates learning agility: Seeks out resources and experience to continually build knowledge/skills; quickly and successfully applies learning to new situations
- Reaches decisions in a timely and transparent manner, knows when enough information has been collected to make sound decisions, takes calculated risks with the confidence to defend their credit view
- Proficient in various spreadsheet and word processing applications
Education:
- Bachelor’s/University degree, Master’s degree preferred
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.
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Job Family Group:
Risk Management
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Job Family:
Credit Risk
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Time Type:
Full time
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Primary Location:
Jacksonville Florida United States
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Primary Location Full Time Salary Range:
$130,880.00 - $196,320.00
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, Constructive Debate, Escalation Management, Financial Analysis, Policy and Procedure, Policy and Regulation, Product Knowledge, Risk Controls and Monitors, Risk Identification and Assessment.
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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:
Jan 15, 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 $130K-$196K 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 ($163K) sits 22% below the category median. Disclosed range: $130K to $196K.
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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