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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
Join a pioneering team at the forefront of financial technology innovation. We are building a cutting-edge, agentic AI platform designed to revolutionize the end-to-end credit risk model development lifecycle. By leveraging the power of Large Language Models (LLMs) and intelligent workflows, we aim to augment our quantitative modelers, dramatically increasing their productivity, enhancing model governance, and accelerating the delivery of critical risk models.
We are seeking a senior, hands-on technology leader to drive this transformation. In this role, you will partner directly with quantitative analysts and key stakeholders to shape the most impactful use cases and then lead a team of talented developers to design, build, and deploy the generative AI platform that brings this vision to life.
Key Responsibilities
- Strategic Vision & Stakeholder Partnership: + Collaborate closely with quantitative model developers, Model Risk Management (MRM), and business leaders to deeply understand their pain points and translate them into a technical vision and product roadmap. + Identify, scope, and prioritize use cases for the agentic workflow platform, ensuring they deliver measurable value and align with strategic objectives. + Serve as the primary technical liaison between the engineering team and its users, ensuring a tight feedback loop and continuous alignment.
- Platform Design & Hands-On Development: + Lead the architectural design of a scalable, robust, and secure agentic AI platform, including core components for orchestration, knowledge retrieval (e.g. RAG), and tool integration. + Engage in hands-on software development to build foundational components, create proofs-of-concept, and tackle the most complex technical challenges. + Design and implement secure integrations with internal data sources, external APIs, and various LLMs, ensuring compliance with all data governance and security policies. + Establish the CI/CD, testing, logging, and monitoring frameworks required for a mission-critical enterprise application.
- Team Leadership & Mentorship: + Lead, mentor, and inspire a team of software engineers, fostering a culture of technical excellence, innovation, and collaboration. + Guide the team's architectural decisions, enforce best practices for code quality, and oversee the agile development process. + Manage project timelines, resource allocation, and delivery of key platform milestones.
Qualifications:
- Extensive Software Engineering Experience: 10+ years of hands-on experience designing, building, and shipping complex, scalable software systems to production.
- Demonstrable experience in AI Agentic solutions such a for ad hoc data analysis, documentations and workflow orchestration
- Team Leadership: Proven experience leading a software development team, including providing technical mentorship, architectural guidance, and project oversight.
- Expertise in Python: Deep proficiency in Python and its ecosystem of libraries for backend development and data processing and modern AI frameworks such as LangChain
- Practical Generative AI Experience: Demonstrable, hands-on experience building applications using Large Language Models (LLMs). This includes a strong understanding of prompt engineering, retrieval-augmented generation (RAG), and agentic patterns.
- Strong Architectural Skills: Solid understanding of system design, microservices architecture, REST APIs, and event-driven systems.
- Excellent Communication: Ability to effectively communicate complex technical concepts to both technical and non-technical stakeholders and to translate business needs into concrete technical requirements.
Nice-to-Have:
- Financial Domain Knowledge: Experience in the financial services industry, particularly in a quantitative finance, risk management, or Model Risk Management (MRM) context.
- Familiarity with Model Development: Understanding of the market and/or credit risk model development lifecycle (e.g., for PD, LGD, EAD models) and associated regulatory requirements (e.g., SR 11-7, CCAR, CECL, etc.).
- Regulated Environments: Experience building auditable and compliant software within a highly regulated environment.
- Full-Stack Experience: Familiarity with frontend technologies and developing user interfaces for technical workflows.
- Broader Language Proficiency: Experience with Java/C++ in addition to Python.
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Job Family Group:
Technology -
Job Family:
Applications Development -
Time Type:
Full time -
Primary Location:
Jersey City New Jersey 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.
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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 02, 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
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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 $176K-$265K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Disclosed range: $176K to $265K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Citi AI Hiring
Citi has 69 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span Bengaluru, Karnataka, India, Getzville, NY, US, Mumbai, Maharashtra, India. Compensation range: $160K - $300K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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