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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 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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We are seeking a highly motivated and innovative Forward Deployed Engineer to join our dynamic team. In this pivotal role, you will be at the forefront of our AI transformation journey, partnering directly with business process owners and technology leaders to fundamentally reimagine and re\-engineer core business processes. You will help define principled approaches to agentic solution design and create reference solutions that leverage the power of Artificial Intelligence to set new standards for efficiency and innovation.
The ideal candidate is a creative problem\-solver with a unique blend of technical expertise, business acumen, and a passion for AI. You will act as the crucial bridge between business needs and technical solutions, identifying opportunities for AI\-driven automation and bringing those concepts to life. A key part of your role will be to drive the adoption of these reference solutions within various business contexts and to apprentice future experts, ensuring the onward sustainability and scalability of our AI capabilities. You will achieve this by leveraging the full breadth and depth of Citi AI capabilities, including our growing set of AI agents.
Key Responsibilities
- Collaborate \& Analyze: Work closely with business stakeholders to conduct in\-depth analyses of existing business processes, workflows, and pain points. Identify and prioritize opportunities where AI can deliver maximum impact.
- Solution Design \& Prototyping: Design and rapidly prototype innovative AI\-powered solutions. This includes developing proof\-of\-concepts (POCs) and where appropriate, minimum viable products (MVPs) to demonstrate feasibility and value.
- Bridge Communication Gaps: Serve as a key technical translator and liaison between non\-technical business users and core engineering teams, ensuring requirements are clearly understood and solutions are fit\-for\-purpose.
- Stay Current: Maintain a deep understanding of the rapidly evolving AI landscape, including new tools, techniques, and best practices in machine learning, natural language processing, and generative AI.
- Measure \& Optimize: Define key performance indicators (KPIs) to measure the effectiveness and ROI of AI initiatives.
- Documentation \& Training: Participate in the creation of comprehensive documentation for new AI\-driven processes and provide training and support to business users to ensure successful adoption.
Qualifications \& Required Skills
- Professional Experience: 3\-5\+ years of experience in a hands\-on technical role such as software engineering, data science, or machine learning engineering.
- AI/ML Knowledge: Deep, hands\-on experience with LLMs and AI agents as well as core machine learning concepts (both supervised and unsupervised), natural language processing (NLP), and data modeling.
- Business Acumen: Demonstrated ability to understand business challenges and translate them into technical requirements and solutions.
- Communication Skills: Exceptional interpersonal and communication skills, with the ability to articulate complex technical concepts to a non\-technical audience.
- Problem\-Solving: A proactive and analytical approach to problem\-solving, with a track record of delivering innovative solutions.
Preferred Skills \& Nice\-to\-Haves
- Client\-Facing Experience: Prior experience in a "forward deployed," consulting, or client\-facing technical role is highly desirable.
- Cloud Platforms: Hands\-on experience with major cloud platforms (AWS, Google Cloud, Azure) and their associated AI/ML services (e.g., SageMaker, Vertex AI, Azure ML).
- Data Engineering: Familiarity with data engineering principles, including ETL/ELT processes, data warehousing, and working with large datasets.
- Domain Expertise: Knowledge of a specific business domain such as payments, finance and operations in financial services firms.
Education:
- Bachelor’s degree/University degree or equivalent experience
What We Offer
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At Citi's Tampa campus, you'll join a high\-performing team working at the leading edge of AI transformation in financial services with access to a collaborative, performance\-driven environment and a benefits package designed to support your professional growth and personal wellbeing.
- Hybrid working model with 3 days in the office and 2 days working remotely, giving you flexibility while staying connected to your team.
- A high\-visibility role at the center of Citi's AI strategy, where your work directly shapes the future of how the organization operates.
- Access to Citi's full suite of AI capabilities and a growing ecosystem of AI agents, giving you cutting\-edge tools to solve complex, real\-world problems.
- Structured learning and development opportunities to deepen your technical expertise and grow your career within one of the world's leading financial institutions.
- Wellbeing and financial support programs designed to help you thrive professionally and personally.
- The opportunity to mentor and develop the next generation of AI practitioners within Citi, building lasting impact beyond your immediate team.
Apply now to help shape Citi's AI transformation — designing solutions that redefine what's possible across one of the world's largest financial organizations.
\#LI\-MB1
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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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Tampa Florida United States
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Primary Location Full Time Salary Range:
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$141,440\.00 \- $212,160\.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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Aug 12, 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 $141K-$212K range is above the median 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 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($176K) sits 18% below the category median. Disclosed range: $141K to $212K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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