Interested in this AI/ML Engineer role at Citi?
Apply Now →Skills & Technologies
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
About the Role
We are looking for a technically deep, strategically minded leader to own Partner AI Development & Delivery and GenAI Model Governance. This role sits at the intersection of Generative AI innovation, enterprise model governance, and regulatory compliance — with end-to-end accountability across high-visibility programs that shape business outcomes at scale.
You will lead a multi-geography team, engage MD-level stakeholders across Operations, Marketing, Products, Technology, Model Risk, Fair Lending, and Legal, and deliver across expense reduction, revenue growth, customer experience improvement, compliance transformation, and enterprise GenAI capability buildout.
What You'll Deliver
1. Banking Operations AI Automation Partner with business units and Technology to deliver GenAI-powered automation across front, mid, and back office banking operations — including call center IVR, Agent Assist, Collections, and other operational functions. Combine technical depth with deep banking domain knowledge to address complex, cross-functional challenges at scale.
2. Foundation GenAI Capabilities Support enterprise-wide foundational GenAI capabilities including LLM fine-tuning and semantic layer development — enabling scalable, data-driven decision-making across business functions.
3. GenAI Solution Development Own the full AI solution lifecycle — concept to production — for high-priority business problems including Personalized Next Best Action decisioning, Campaign Analytics, and Conversational & Contextual Business Insights.
4. Analytics Workflow Transformation Conceptualize AI-powered tools that accelerate analytics workflows and drive productivity. Lead the evolution of AI-enhanced modeling techniques to improve development speed and precision.
5. GenAI Model Portfolio Governance Manage a large GenAI model portfolio across its full lifecycle — tuning, upgrades, and maintenance. Serve as Model Sponsor Delegate, with formal accountability for model ownership, sign-off, and governance under Model Risk Policy.
6. Enterprise GenAI Capabilities Build firm-wide GenAI infrastructure: adversarial testing, regulatory testing, guardrails, near real-time monitoring, and AI governance policy co-development with the second line of defense.
7. Measurable Productivity & Delivery Establish frameworks that track outcomes across business deliverables and team performance — aligned with senior leadership priorities.
How You'll Deliver
Build a High-Performing AI Team Recruit top talent and lead a specialized team delivering enterprise-grade AI. Foster a culture of innovation focused on non-linear outcomes.
Partner with Central AI and Business Teams Execute strategically with the firm's Central AI organization and business units to redesign processes and deliver measurable change.
Align on Priorities Work closely with business stakeholders and analytics leadership to identify, prioritize, and resource high-impact AI solutions — enabling rapid alignment and efficient delivery.
Maintain a Robust Controls Mindset Ensure teams follow control processes across Information Security, Platform Security, and Data Security while executing on next-generation AI re-engineering of business and data processes.
Qualifications
Experience
- 10+ years in AI/ML, data science, or quantitative analytics within financial services
- Proven expertise in Generative AI, LLMs, and applied GenAI product development in production environments
- Track record in model risk management, regulatory remediation, and formal model governance
- Experience managing large-scale AI/ML model portfolios including lifecycle, tuning, and upgrades
- Deep data domain knowledge across core banking functions — including marketing, customer management, call center operations, collections, complaints, and financial controls — with the ability to connect data signals to business outcomes
- Strong understanding of credit card economics, customer behavior, and product strategy at the intersection of data and finance
- Experience with Model Risk Management, Fair Lending, and Compliance in a regulated financial institution
Technical Skills
- GenAI & ML: Generative AI, LLMs, RAG, Agentic AI, prompt engineering, LLM fine-tuning, NLP, supervised/unsupervised learning, Mathematical Optimization & Decision Science
- Infrastructure & Ops: MLOps/LLMOps, cloud-native AI platforms, big data, distributed computing, Spark, Python, SQL
- Governance: Adversarial testing, guardrails, regulatory testing frameworks, near real-time model monitoring
- Tooling: SDLC, version control (Bitbucket/GitHub), JIRA, DevOps
- Regulatory: SR 11-7, SR 26-2, OCC AI guidance, CFPB, Fair Lending, ECOA — in the context of AI/ML deployment within a regulated institution
Leadership & Communication
- Proven ability to lead distributed, multi-geography teams in a global, matrixed organization
- Exceptional stakeholder management — engage, influence, and negotiate at the MD level
- Strong program management across complex, multi-workstream initiatives with regulatory and executive timelines
- Ability to translate technical complexity into clear narratives for senior audiences
- Board-ready executive communication and presentation skills
Education
- Bachelor's degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Engineering, or Economics) required
- Master's degree or Ph.D. strongly preferred
-
Job Family Group:
Decision Management -
Job Family:
Specialized Analytics (Data Science/Computational Statistics) -
Time Type:
Full time -
Primary Location:
New York New York United States -
Primary Location Full Time Salary Range:
$192,000.00 - $288,000.00
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.
-
Most Relevant Skills
Please see the requirements listed above. -
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter. -
Anticipated Posting Close Date:
Sep 11, 2026 -
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
-
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 $192K-$288K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 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 $212,040 based on 13,780 positions with disclosed compensation. This role's midpoint ($240K) sits 13% above the category median. Disclosed range: $192K to $288K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Citi AI Hiring
Citi has 56 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, AI Agent Developer, Data Engineer. Positions span Tampa, FL, US, Tampa, FL, Mississauga, Ontario, Canada. Compensation range: $130K - $300K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.