Interested in this AI/ML Engineer role at Citi?
Apply Now →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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*At Citi, you won't be running a typical IT implementation. You'll be at the center of redefining how one of the world's largest financial institutions actually operates — from the inside out. Services Operations touches millions of client interactions, thousands of associates, and some of the most complex workflows. You'll have the mandate, the access, and the executive backing to deploy AI where it matters most: at the point where our people serve our clients. The problems are real, the scale is unmatched, and the impact is immediate and measurable.*
Citi is looking for a Director, Operations Agentic Workflow \& Implementation to lead the design and scaled delivery of agentic AI solutions that fundamentally transform how operational processes are executed across the firm partnering with product, operations, technology and second line partners. Sitting at a senior level within Citi's Operations function, this role carries full accountability for embedding AI\-driven automation into production environments \- bridging strategy, governance, and hands\-on implementation to deliver measurable business outcomes.
This is not a role for someone who theorises about AI transformation — it is for a seasoned operational technologist who has lived the realities of high\-volume, compliance\-sensitive service environments and knows how to deploy intelligent automation at scale. If you have a track record of driving large\-scale process transformation through emerging technology, this is an opportunity to shape the future of how Citi operates at scale.
Responsibilities
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- Lead the end\-to\-end design and implementation of agentic AI workflows, delivering automation solutions that optimise operational processes at scale across the firm.
- Define and drive the strategic direction for AI\-enabled operations, establishing implementation standards, governance frameworks, and performance metrics that ensure resilience and appropriate human oversight.
- Partner with Operations, Technology, Product, and Risk teams to redesign existing processes, embed AI agents effectively, and maximise both productivity and service quality.
- Implement a measurement framework that tracks and continuously improves business outcomes — including cost efficiency, cycle time reduction, accuracy, operational risk reduction, and employee experience.
- Manage the transition of AI solutions from pilot through to scaled production, ensuring seamless integration into live operational environments with robust controls in place.
- Lead and develop multiple senior operations managers across large or distributed teams, setting clear performance expectations and making decisions on hiring, development, and team structure.
- Negotiate and influence at senior leadership level across functions, ensuring alignment of AI implementation priorities with broader business strategy and risk appetite.
Required Qualifications \& Skills
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- 15 or more years of experience in operations or a related function, including at least 8 to 10 years leading large, matrixed teams through other managers.
- Demonstrated ability to lead medium\- to long\-term strategic planning across complex, cross\-functional environments, with full accountability for budgets, results, and policy decisions.
- Deep knowledge of operational process design and implementation, with a clear understanding of how AI and workflow automation can be applied to improve end\-to\-end service delivery.
- Commercial acumen and analytical capability, including a working understanding of risk and reward dynamics and how they contribute to sustainable operational performance.
- Ability to assess risk in operational and business decisions, with a consistent approach to compliance, controls, and ethical conduct that protects the firm's reputation and client assets.
- Excellent communication and negotiation skills, with a track record of influencing senior stakeholders across multiple functions and engaging effectively with external parties.
Profile
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We are looking for candidates who fit one or more of the following profiles:
- Operations or Operations Technology Leaders: Professionals who have led technology\-enabled transformation within operations functions at large regulated institutions. They understand the realities of high\-volume, compliance\-sensitive service environments — SLAs, case management, escalation paths, and the human impact of getting automation wrong.
- AI/Automation Program Leaders: Senior leaders who have implemented AI and intelligent automation specifically within operations contexts — solving real workflow problems rather than building proofs of concept that never reach production.
- Transformation Leaders: Individuals who have deployed and scaled AI and agentic solutions, implemented intelligent routing, and delivered agent\-assist tools that operations teams actually use and trust.
Key Attributes
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- Operational Empathy: Has walked in the shoes of an operations team. Understands the day\-to\-day realities of SLAs, case management, escalation paths, and the human impact of getting automation wrong.
- Pragmatic Technologist: Possesses strong working knowledge of AI — including LLMs and agentic frameworks — but filters every technical decision through the lens of operational feasibility, auditability, and risk. Can credibly partner with both product/engineering teams and operations leaders.
- Builds for Operators: Obsessed with implementing tools that are intuitive, reliable, and trusted by the people who depend on them daily.
- Change Agent with Discipline: Comfortable navigating the ambiguity of emerging AI capabilities, while remaining grounded in the governance and control requirements of a regulated financial institution. Brings structure to transformation without stifling momentum.
- Proven Delivery at Scale: Has a track record of deploying AI or automation solutions that produced measurable operational outcomes — reduced average handle time, improved first\-contact resolution, or lower cost\-per\-transaction — rolled out across large operations teams (hundreds to thousands of users) with evidence of adoption, change management, and sustained impact.
- Cross\-Functional Operator in Regulated Environments: Experienced in delivering technology initiatives in close partnership with Operations, Risk, Compliance, and Technology — navigating governance frameworks without losing delivery momentum.
- Process\-First Mindset: Demonstrates a consistent habit of understanding and re\-engineering the underlying process before layering in automation — not simply digitising broken workflows.
Beneficial Skills \& Qualifications
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- Prior experience in a specific line of business within financial services operations, providing relevant context for the scope and complexity of this role.
- Familiarity with agentic AI technologies, orchestration platforms, or large\-scale process automation programmes within an enterprise environment.
- Experience working in close collaboration with Technology and Product functions to co\-design and deliver AI or automation solutions into operational workflows.
What We Offer
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This role offers the opportunity to lead one of the most strategically significant programmes within Citi's Operations function — with real ownership, genuine influence, and the scale of a global financial institution behind you. You will work alongside senior leaders across the firm, with the resources and mandate to deliver transformation that matters.
- A hybrid working model with 3 days in the office and 2 days working remotely, giving you flexibility without sacrificing connection to your teams and stakeholders.
- Senior\-level ownership of a high\-visibility programme, with the authority to shape strategy, set direction, and make decisions that have lasting impact on the firm.
- Access to Citi's global network and cross\-functional leadership community, enabling collaboration at the intersection of operations, technology, and AI innovation.
- Competitive compensation and financial wellbeing benefits commensurate with a Director\-level position at a leading global financial institution.
- Ongoing learning and professional development opportunities to stay at the forefront of AI\-enabled operations and enterprise transformation.
- Wellbeing support and work\-life balance resources designed to help you perform at your best across all dimensions of your career and personal life.
*Apply now to take strategic ownership of Citi's agentic AI operations agenda and deliver transformation that redefines how a global institution works.*
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Job Family Group:
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Business Strategy, Management \& Administration
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Job Family:
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Business Process Re\-Engineering
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Time Type:
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Full time
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Primary Location:
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New York New York 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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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 $170K-$300K range is above the 75th percentile 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($235K) sits 9% above the category median. Disclosed range: $170K to $300K.
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
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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