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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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Spearhead AI\-driven Transformation in Investment Banking
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We are seeking an exceptionally experienced and highly proficient Director of Applied AI Engineering to join our Investment Banking technology leadership team. This is a critical, C15\-level role demanding deep technical expertise, a strategic mindset, and a strong client\-facing orientation. The successful candidate will be a pioneer in integrating cutting\-edge AI/ML solutions directly into client workflows across Equity Capital Markets (ECM), Debt Capital Markets (DCM), and Mergers \& Acquisitions (M\&A).
Operating with a high degree of autonomy, you will solve complex, high\-impact problems, set technical direction, and champion an AI\-first approach within a dynamic, client\-centric environment. You will be instrumental in translating intricate business challenges in the financial domain into robust, scalable, and high\-performance AI systems that deliver tangible value and competitive advantage to our clients and internal stakeholders. This role requires a techno\-functional leader who can bridge the gap between advanced AI capabilities and critical business outcomes, fostering direct engagement with clients and business leads.
Key Responsibilities:
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- Strategic AI/ML Solution Design \& Implementation (Client\-Focused): Lead the end\-to\-end design, architecture, and hands\-on implementation of advanced AI/ML solutions and platforms directly supporting and enhancing Investment Banking client workflows in ECM, DCM, and M\&A. This includes leveraging and adapting existing models, and pioneering new AI\-driven approaches to meet specific client needs and strategic business objectives. Engage directly with clients to gather requirements, present solutions, and ensure successful adoption.
- Principal AI/ML Engineering \& Technical Leadership: Act as a primary subject matter expert and thought leader in advanced AI/ML engineering, especially within the context of Investment Banking products. Provide overarching technical leadership, guidance, and mentorship to engineering teams and business stakeholders, fostering best practices in AI\-augmented development, scalable system design, code reviews, and collaborative problem\-solving. Champion a culture of quality through disciplined application of Spec\-Driven Development (SDD), Test\-Driven Development (TDD) / Behavior\-Driven Development (BDD), and promote AI\-driven test case generation and quality automation.
- Architect for Scalability, Resilience \& Security: Provide deep expertise in modern application architecture, designing for cloud readiness by applying 12\-Factor App principles and microservice patterns. Ensure AI solutions are built for optimal performance, scalability, resilience, and security within high\-compliance environments (on\-premise, hybrid cloud, or private cloud).
- Techno\-Functional Partnership \& Domain Expertise: Develop a deep understanding of Investment Banking products (ECM, DCM, M\&A), collaborating closely with business stakeholders and external clients to identify critical business needs and high\-impact AI opportunities. Act as a strategic partner, translating complex financial requirements into technical specifications and delivering AI solutions that directly address client pain points.
- Data Strategy \& Engineering Leadership: Work closely with data engineers and data scientists to define advanced data requirements, ensure exceptional data quality, and optimize complex data pipelines for robust AI solution integration and deployment. Drive strategies for utilizing both structured financial datasets and unstructured data sources (filings, call transcripts, research) effectively.
- Model Deployment, Monitoring \& Optimization: Oversee the deployment, scaling, monitoring, and continuous maintenance of AI/ML solutions in production environments. Implement advanced performance optimizations for AI solutions and underlying infrastructure to ensure efficient resource utilization, rapid inference, and proactive issue resolution.
- Strategic Vision, Innovation \& Advocacy: Contribute significantly to the strategic vision for AI in Investment Banking by researching, evaluating, and advocating for new AI technologies, methodologies, and tools (e.g., LLMs, prompt engineering, Retrieval\-Augmented Generation \- RAG, MCP, A2A). Drive adoption of AI\-powered tools (Devin, GitHub Copilot, Claude, Codex) to accelerate development, automate complex tasks, and validate architectural patterns across the SDLC. Embrace an agile, iterative mindset that avoids "Big Up\-Front Design" (BUFD).
- Full\-Stack Problem Solving \& Database Mastery: Mastermind complex database interactions across Oracle, SQL, and MongoDB, employing AI to analyze query performance and recommend optimizations. Resolve high\-impact problems across the entire stack through in\-depth evaluation of business and system processes.
Qualifications \& Experience:
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- 10\+ years of experience in software engineering, with at least 5\+ years in a senior or lead Applied AI/ML engineering role specifically delivering complex, enterprise\-grade, client\-facing applications in financial services.
- Demonstrated success in building and deploying innovative AI applications within financial services, banking, or capital markets domains, with significant exposure to Investment Banking products (ECM, DCM, M\&A).
- Proven experience leading technical implementations, mentoring other senior engineers, and directly engaging with clients in a principal capacity.
- Deep expertise in ML, NLP, LLMs, Retrieval\-Augmented Generation (RAG), embeddings, and modern MLOps practices.
- Strong experience working with both structured financial datasets and unstructured data sources (e.g., SEC filings, call transcripts, research) within a regulated environment.
- Familiarity with front\-office workflows in ECM, DCM, M\&A, and investment research is essential.
- Exceptional communication and stakeholder management skills, with the ability to articulate complex technical and business concepts to diverse audiences, including senior executives and external clients.
- Advanced degree (Master's or Ph.D. preferred) in Computer Science, AI, Applied Mathematics, Engineering, or a related quantitative field.
Must\-Have Engineering \& Technical Acumen:
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- Engineering Principles: Deep, practical knowledge of designing for cloud readiness, including microservice architecture, 12\-Factor App principles, and modern design patterns.
- Development Methodologies: Proven experience championing and advocating for SDD (Spec Driven Development), TDD (Test Driven Development), and DDD (Domain Driven Design) within an agile environment.
- Core Backend Stack: Mastery of modern Java (JDK 17/21\) and the core Spring Framework (Spring Boot, Spring MVC, Spring Data JPA, Spring Cloud).
- Frontend Technologies: Hands\-on experience with modern UI frameworks like Angular, using TypeScript/JavaScript to build intuitive, client\-facing user interfaces.
- Databases: Strong, hands\-on experience with both relational (Oracle, SQL) and NoSQL (MongoDB) databases, with an emphasis on AI\-driven optimization.
- DevOps \& CI/CD: Proficiency with modern CI/CD pipelines (e.g., Harness, Jenkins), containerization with Docker, and container orchestration platforms (OpenShift, Kubernetes).
AI\-First Expertise:
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- Practical Application: Demonstrable, hands\-on experience using and integrating AI development tools (e.g., Devin, GitHub Copilot, Claude, Codex) throughout the software development lifecycle for code generation, debugging, documentation, and automated quality assurance.
- Strategic Mindset: Strong understanding of the concepts underpinning modern AI tools (LLMs, prompt engineering, Retrieval\-Augmented Generation \- RAG, MCP, A2A) and a clear vision for leveraging them to transform engineering productivity, quality, and client value.
What Success Looks Like:
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- Seamless integration of AI tools into daily workflows of Investment Banking clients and analysts, demonstrably enhancing their productivity and decision\-making.
- Significant reduction in manual effort across critical client\-facing activities such as targeting, pitch preparation, and market monitoring.
- AI\-driven data assets and ML models fully aligned with enterprise governance, architecture, and client\-specific requirements.
- A scalable AI platform that rapidly evolves to meet changing business needs and client demands, consistently delivering innovative solutions.
MUST HAVE: financial services, banking, or capital markets domains, with significant exposure to Investment Banking products (ECM, DCM, M\&A).
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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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Jersey City New Jersey United States
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Primary Location Full Time Salary Range:
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$170,000\.00 \- $300,000\.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.
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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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Jun 19, 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.*
Salary Context
This $170K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($235K) sits 30% above the category median. Disclosed range: $170K to $300K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Citi AI Hiring
Citi has 17 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, AI Product Manager. Positions span New York, NY, US, Jacksonville, FL, US, Jersey City, NJ, US. Compensation range: $106K - $500K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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