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
Citi is seeking a Senior AI Application Development Lead to spearhead the architecture and delivery of intelligent, distributed systems at the heart of one of the world's premier financial institutions. In this role, you will lead an innovative engineering team within Citi Wealth Technology, driving strategic solutions across Artificial Intelligence, Machine Learning, and Distributed Computing for our global wealth and private banking client base.
This role is structured as 60% technical leadership, team mentorship, and architectural governance, and 40% hands-on systems design, prototyping, and core development. You will guide engineering squads, elevate development standards, and own end-to-end architecture across complex AI platforms — spanning modern LLM orchestration frameworks, cloud-native infrastructure, and scalable backend services.
Key Responsibilities
Technical Leadership & Mentorship (60%)
- Engineering Direction & Governance: Provide technical direction and architectural oversight across multiple engineering squads within Wealth Technology, ensuring adherence to enterprise design principles, scalability standards, and engineering best practices.
- Team Mentoring & Talent Development: Coach and mentor mid-level to senior engineers, conducting high-impact code reviews, technical deep-dives, and career growth coaching.
- Modern AI Engineering Practices: Drive team adoption of modern developer productivity tools, including AI coding agents (such as GitHub Copilot and Devin), establishing frameworks for accelerated software delivery.
- Stakeholder Alignment & Solution Strategy: Partner with business leaders, product managers, and cross-functional teams to translate strategic business requirements into robust, forward-looking technical roadmaps using Jira, Confluence, and GitHub.
- Security & Quality Governance: Champion DevSecOps principles, embedding automated testing, code quality, and security scanning (via Checkmarx, Snyk, Black Duck) across all CI/CD pipelines.
Hands-On Architecture & Development (40%)
- AI & Intelligent Systems Design: Architect, prototype, and build production-grade AI/ML solutions using Python and Java, integrating LLM orchestration frameworks (LangChain, LlamaIndex) and leveraging Google Vertex AI agent services.
- Distributed & Event-Driven Systems: Architect high-throughput, fault-tolerant event streams and data pipelines utilizing Apache Kafka, Apache Flink, and modern distributed streaming patterns.
- API & Backend Service Engineering: Design and implement resilient, low-latency microservices and API gateways using FastAPI, Flask, or Spring Boot, backed by PostgreSQL and Redis.
- Cloud & Container Infrastructure: Deploy and manage resilient containerized applications on Kubernetes and Red Hat OpenShift, utilizing Helm charts, CI/CD automation (Jenkins, Tekton, Harness), and observability suites (Prometheus, Grafana, AppDynamics, Splunk).
- Embed security and code quality practices across the engineering lifecycle using tools such as Checkmarx, Snyk, and BlackDuck, ensuring all solutions meet enterprise security and compliance standards.
Required Qualifications & Skills
- 12+ years of progressive software engineering and architecture experience, with a proven track record of technical leadership and team mentorship.
- Leadership & Architecture: Demonstrated experience leading engineering teams, driving architectural decisions, conducting technical design reviews, and fostering engineering excellence.
- Core Languages: Deep hands-on proficiency in Python and/or Java, with strong database design and querying skills in SQL.
- Production AI & LLM Systems: Hands-on experience architecting and delivering AI/ML and LLM-driven applications, including retrieval-augmented generation (RAG), prompt engineering, chunking strategies, and orchestration frameworks (LangChain, LlamaIndex, or Vertex AI APIs).
- Distributed Systems & Event Streaming: Strong expertise in designing distributed, event-driven architectures with streaming platforms such as Apache Kafka or Apache Flink.
- Cloud-Native & Container Platforms: Substantial experience deploying and managing workloads on Kubernetes or Red Hat OpenShift, with cloud platform expertise on AWS or GCP and Helm chart management.
- Data Architecture: Strong foundation in relational database architecture (e.g., PostgreSQL) and caching/in-memory solutions (e.g., Redis).
- Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.
- A genuine commitment to writing clean, maintainable, and reusable software, paired with a strong sense of ownership and accountability for delivered outcomes.
Beneficial Skills & Qualifications
- Experience with modern AI coding agents such as Devin or GitHub Copilot, or equivalent AI-assisted development tools used to accelerate engineering delivery.
- Frontend & Full-Stack Development: Practical experience or familiarity with modern frontend frameworks and libraries (e.g., React, Next.js, Angular, TypeScript, and Tailwind CSS) to facilitate end-to-end prototyping and seamless collaboration with frontend teams.
- Multi-Agent Systems: Exposure to multi-agent frameworks (e.g., Google ADK, CrewAI, AutoGen, LangGraph).
- Familiarity with workflow scheduling and orchestration tools such as Apache Airflow or AutoSys for managing complex data and processing pipelines.
- Experience configuring and interpreting observability tooling across Prometheus, Grafana, AppDynamics, and Splunk in a production environment.
What We Offer
At Citi, you will work on technology challenges that operate at genuine global scale — systems that underpin financial markets, private banking, and client experiences across more than 160 countries. This is a role for engineers who want strategic ownership, deep technical engagement, and the opportunity to define how AI and cloud-native platforms are applied in one of the world's most complex financial institutions.
- Hybrid working model — 3 days in the office and 2 days working remotely, giving you flexibility alongside meaningful team collaboration.
- Ownership of high-impact AI and distributed systems projects, with the scope to influence architecture decisions across Wealth Technology organization.
- Access to a modern, broad technology stack — spanning LLM frameworks, cloud platforms, full-stack engineering, and AI-assisted development tools — with the space to experiment, prototype, and implement forward-looking solutions.
- A performance-driven environment where your contributions are recognized and your career progression is tied to the impact you deliver.
- Mentorship and leadership opportunities, with the ability to shape the technical direction and engineering culture of a high-caliber AI team.
- Competitive financial wellbeing and benefits package, reflective of the seniority and strategic nature of this position.
Apply now to bring your AI engineering and development expertise to a team building the intelligent systems that define the future of global banking.
-
Job Family Group:
Technology -
Job Family:
Applications Development -
Time Type:
Full time -
Primary Location:
New York New York United States -
Primary Location Full Time Salary Range:
$176,720.00 - $265,080.00
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:
Oct 16, 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 $176K-$265K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Disclosed range: $176K to $265K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Citi AI Hiring
Citi has 44 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, Data Scientist, AI Software Engineer. Positions span Jersey City, NJ, US, Jersey City, NJ, New York, NY. Compensation range: $130K - $300K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.