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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 more than 230,000 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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Responsibilities (Leveraging AI):
Lead the design, development, and implementation of complex middleware applications using Java and Spring Boot:Utilize AI\-powered code generation tools (e.g., Devin, Copilot, Codex) to accelerate development, automate boilerplate code, suggest optimal implementations, and enforce architectural patterns. Leverage AI for design pattern identification and complex system architecture validation.
Architect and optimize database interactions with Oracle, SQL, and MongoDB, ensuring high performance and data integrity: Employ AI to analyze database query performance, suggest advanced indexing strategies, optimize schema designs, and generate efficient SQL/NoSQL queries. AI\-driven tools can also assist in predicting database load and recommending scaling solutions.
Drive the adoption and continuous improvement of CI/CD pipelines to facilitate rapid and reliable software delivery: Integrate AI into CI/CD processes for intelligent test case generation, predictive failure analysis, automated code vulnerability scanning, and optimization of pipeline execution times based on historical data.
Collaborate with cross\-functional teams, including product management, QA, and operations, to define requirements, design solutions, and deliver high\-quality software:Use AI\-powered communication and summarization tools (e.g., Claude) to streamline requirement gathering, document analysis, and stakeholder communication. AI can also assist in translating technical designs into accessible formats for various audiences.
Mentor and provide technical guidance to junior and mid\-level software engineers, fostering a culture of technical excellence and continuous learning: Leverage AI platforms for personalized learning paths, automated code feedback, and explanations of complex technical concepts. Encourage junior engineers to adopt AI\-driven development practices.
Actively research and experiment with AI technologies to identify opportunities for enhancing developer productivity, automating tasks, and improving software quality:Continuously explore emerging AI tools and techniques (such as Anti Gravity for complex problem\-solving) and assess their applicability to our development ecosystem.
Participate in code reviews, ensuring adherence to coding standards, best practices, and architectural guidelines: Utilize AI\-powered code analysis tools to pre\-scan code for potential bugs, security vulnerabilities, performance bottlenecks, and style deviations, allowing human reviewers to focus on higher\-level logic and design.
Troubleshoot and resolve complex technical issues, ensuring the stability and performance of production systems:Implement AI\-driven anomaly detection in monitoring systems, leverage AI for rapid log analysis and root cause identification, and automate incident response workflows.
Contribute to the strategic planning and technical roadmap for our middleware platforms: Employ AI to analyze industry trends, forecast technology evolution, assess the impact of new features, and prioritize roadmap initiatives based on data\-driven insights.
Conduct tasks related to feasibility studies, time and cost estimates, IT planning, risk technology, applications development, and model development: Utilize AI for data synthesis, predictive modeling for estimations, identification of potential IT risks, and accelerated model prototyping and validation.
Monitor and control all phases of the development process (analysis, design, construction, testing, and implementation) and provide user and operational support:Implement AI\-driven dashboards and reporting for real\-time project health, automated progress tracking, and proactive identification of development lifecycle bottlenecks.
Utilize in\-depth specialty knowledge of applications development to analyze complex problems/issues, provide evaluation of business process, system process, and industry standards, and make evaluative judgment: Augment human analytical capabilities with AI\-powered data analysis and pattern recognition to rapidly assess complex scenarios and inform robust decision\-making.
Recommend and develop security measures in post\-implementation analysis of business usage to ensure successful system design and functionality: Use AI\-driven security analysis tools to continuously monitor applications for vulnerabilities, predict potential threats, and recommend adaptive security controls.
Consult with users/clients and other technology groups on issues, recommend advanced programming solutions, and install and assist customer exposure systems:Employ AI to quickly understand user pain points, simulate solution impacts, and generate clear documentation or prototypes for client review.
Ensure essential procedures are followed and help define operating standards and processes: Utilize AI for automated compliance checks against defined procedures and for suggesting improvements to operational workflows based on performance data.
Serve as advisor or coach to new or lower\-level analysts: Provide AI\-assisted guidance and training resources, offering personalized feedback and access to extensive knowledge bases.
Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets: Leverage AI for advanced risk analytics, scenario planning, and compliance monitoring, enhancing the accuracy and foresight of risk assessments.
Required Technical Skills:
Core Java: Strong understanding of Java (JDK 8\+, preferably Java 11/17\), including multithreading, collections, garbage collection, and JVM internals.
Frameworks: Extensive experience with Spring Framework (Spring Boot, Spring MVC, Spring Data JPA, Spring Security).
Middleware: Proven experience in designing and developing RESTful APIs and microservices.
Database Technologies (Must):
Relational Databases: Strong proficiency in SQL and experience with Oracle databases, including schema design, query optimization, and stored procedures.
NoSQL Databases: Experience with MongoDB, including data modeling, querying, and performance tuning.
CI/CD \& DevOps: Hands\-on experience with CI/CD tools and practices (e.g., Jenkins, GitLab CI, GitHub Actions, Maven/Gradle, Docker, Kubernetes).
Version Control: Proficiency with Git and standard branching strategies (e.g., Gitflow).
Testing: Experience with unit testing frameworks (JUnit, Mockito) and integration testing.
Web Technologies (Beneficial): Familiarity with web services (SOAP/REST), XML, JSON.
AI Tools \& Methodologies (Must):
Demonstrable exposure and practical experience with AI development tools such as Devin, GitHub Copilot, Claude, Anti Gravity, and Codex.
Strong understanding of AI/ML concepts, prompt engineering, and integrating AI into software development workflows (e.g., for code generation, testing, debugging, and documentation).
A keen desire to apply AI technologies to improve software development processes and outcomes
Recommended Qualifications:
- 6\-10 years of relevant experience in Apps Development or systems analysis role
- Demonstrated expertise in Java, with a deep understanding of its core concepts, libraries, and the Java Virtual Machine (JVM).
- Extensive experience with the Spring Framework, including Spring Boot, for building enterprise\-level applications, and proficiency with ORM frameworks like Hibernate or JPA.
- Strong foundation in object\-oriented programming (OOP) principles and a practical understanding of design patterns to create scalable and maintainable code.
- Proficient in working with relational databases (e.g., PostgreSQL, Oracle, MySQL) and NoSQL databases like MongoDB.
- Experience with unit testing frameworks (e.g., JUnit, Mockito) and a commitment to writing clean, well\-documented, and testable code.
- Solid understanding of RESTful API design and development, and experience creating and consuming web services.
- Extensive experience system analysis and in programming of software applications
- Experience in managing and implementing successful projects
- Subject Matter Expert (SME) in at least one area of Applications Development
- Ability to adjust priorities quickly as circumstances dictate
- Demonstrated leadership and project management skills
- Consistently demonstrates clear and concise written and verbal communication
Education:
- Bachelor’s degree/University degree or equivalent experience
- 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:
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Technology
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Job Family:
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Applications Development
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Time Type:
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Full time
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Primary Location:
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Irving Texas United States
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Primary Location Full Time Salary Range:
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$125,760\.00 \- $188,640\.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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Jul 17, 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 $125K-$188K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($157K) sits 28% below the category median. Disclosed range: $125K to $188K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Citi AI Hiring
Citi has 9 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, US, Jersey City, NJ, US, Tampa, FL, US. Compensation range: $160K - $500K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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