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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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About Citi:
Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management.
As a bank with a brain and a soul, Citi creates economic value that is systemically responsible and in our clients’ best interests. As a financial institution that touches every region of the world and every sector that shapes your daily life, our Enterprise Operations \& Technology teams are charged with a mission that rivals any large tech company. Our technology solutions are the foundations of everything we do from keeping the bank safe, managing global resources, and providing the technical tools our workers need to be successful to designing our digital architecture and ensuring our platforms provide a first\-class customer experience. We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services.
Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well\-rounded backgrounds who bring their authentic selves to work and complement our culture of delivering results with pride. If you are a problem solver who seeks passion in your work, come join us. We’ll enable growth and progress together.
About the Team:
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The Lead Solutions Architect is a strategic professional positioned at the intersection of credit card domain expertise and cutting\-edge generative AI innovation. This role offers a unique opportunity to build foundational knowledge in one of Citi's most dynamic business areas—U.S. Credit Cards—while working alongside some of the brightest engineering minds in the organization. We seek an intellectually curious, technically skilled individual who thrives on learning, embraces complexity, and is eager to shape the future of AI\-driven financial services from the ground up.
The successful candidate will embark on a transformative journey, mastering credit card domain knowledge as the essential foundation for applying generative AI and machine learning to real\-world business challenges. You will contribute to building the technical and domain expertise required for the Forward Deployment Engineer role, working hands\-on with backend systems, web applications, databases, and middleware while gaining exposure to modern DevOps practices, cloud platforms, and containerization. Your work will directly impact how Citi leverages AI to reimagine customer experiences, streamline operations, and deliver innovative solutions at scale—all while learning from world\-class engineers who are redefining what's possible in financial technology.
Responsibilities:
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- Provides architectural vision for assigned IT systems, including those that support Internet applications, ensuring that architecture conforms to enterprise blueprints.
- Develops architecture, strategy, planning, and problem solving solutions on assigned systems. Interfaces across several channels, acting as a visionary to proactively assist in defining direction for future projects.
- Maintains continuous awareness of business, technical, and infrastructure issues and acts as a sounding board or consultant to aid in the development of creative solutions.
- Depending on project scope, may be accountable for end\-to\-end results including such items as: budgeting, policy formulation as well as providing future state technology strategies for an effort.
- Interfaces with vendors to assess their technology and to guide their product roadmap based on Citi requirements.
- Exhibits in\-depth knowledge of how own specialism contributes to the business and has a good understanding of the commercial environment.
- Provides thought leadership in subjects that are key to the business.
- Requires sophisticated analytical thought to resolve issues in a variety of complex situations. Impacts the technology function through contribution to technical direction and strategic decisions.
- Uses developed communication skills to negotiate and often at higher levels.
- Performs other job duties and functions as assigned. 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.
- Design Solution Architecture For Cloud Native Applications
- Selecting Project Technology Stack
- Perform Project Architecture Reviews
- Cloud Architecture Reviews (CART)
- Partner with all internal/external stakeholders to provide solutions to business and engineering team
Qualifications:
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- 6\+ years of hands\-on technical experience in software development, with strong proficiency in backend programming and system design.
- Hands on Experience of technologies like Java, Tibco, Python, AI/ML, COBOL
- Deep expertise in backend programming languages such as Java (Spring Boot, Spring Framework, Hibernate), Python 3\.x (FastAPI, Flask, Django), C\#, or Node.js, with proven ability to build scalable, production\-grade applications.
- Strong proficiency in web development technologies including HTML5, CSS3, JavaScript (ES6\+), TypeScript, and modern frontend frameworks such as React, Angular, or Vue.js.
- Hands\-on experience with relational databases such as Oracle, PostgreSQL, MySQL, or SQL Server, including schema design, SQL query optimization, indexing strategies, and transaction management.
- Experience with NoSQL databases such as MongoDB, Cassandra, DynamoDB, or Redis for handling unstructured or semi\-structured data at scale.
- Proven experience with middleware technologies such as Apache Kafka, RabbitMQ, IBM MQ, TIBCO, or MuleSoft for building event\-driven architectures and integrating enterprise systems.
- Strong understanding of RESTful API design and development, including API versioning, authentication (OAuth 2\.0, JWT), and best practices for microservices communication.
- Experience with message\-oriented middleware (MOM) and enterprise service bus (ESB) patterns for asynchronous communication and system integration.
- Familiarity with CI/CD tools and practices such as Jenkins, GitLab CI/CD, GitHub Actions, or Azure DevOps, including building automated pipelines for testing and deployment.
- Knowledge of cloud platforms such as AWS (EC2, S3, Lambda, RDS), Microsoft Azure (App Service, Functions, Cosmos DB), or Google Cloud Platform, with experience deploying and managing cloud\-based applications.
- Experience with containerization technologies such as Docker and orchestration platforms like Kubernetes, OpenShift, or Amazon EKS for deploying microservices.
- Understanding of DevOps principles and practices, including infrastructure as code (Terraform, CloudFormation), configuration management, and continuous delivery.
- Strong knowledge of version control systems such as Git, including branching strategies, pull request workflows, and collaborative development practices.
- Familiarity with monitoring and logging tools such as Splunk, ELK Stack, Prometheus, Grafana, or Datadog to ensure application reliability and performance.
- Understanding of security best practices including secure coding standards, encryption, authentication, authorization, and compliance requirements in financial services.
- Eagerness to learn credit card domain knowledge, including payment processing, fraud detection, customer management, and regulatory compliance.
- Strong analytical and problem\-solving skills with the ability to troubleshoot complex technical issues and optimize system performance.
- Consistently demonstrates clear and concise written and verbal communication skills with the ability to collaborate effectively with cross\-functional teams.
- Self\-starter with intellectual curiosity, a passion for learning new technologies and business domains, and the ability to work independently.
- Ability to thrive in a fast\-paced, dynamic environment while managing multiple priorities and delivering high\-quality results.
Education:
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Bachelor's/University degree in Computer Science, Engineering, Information Systems, or related technical field, or equivalent experience; Master's degree preferred.
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Job Family Group:
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Technology
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Job Family:
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Architecture
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Time Type:
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Full time
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Primary Location:
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Jacksonville Florida United States
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Primary Location Full Time Salary Range:
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$113,840\.00 \- $170,760\.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 14, 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 $113K-$170K range is below the median 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($142K) sits 34% below the category median. Disclosed range: $113K to $170K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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