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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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The USCC Architecture and AI Engineering group is at the forefront of technological innovation, and we are looking for a highly motivated and talented Generative AI/AI Engineer to join our dynamic team. This is an exciting opportunity to work on cutting\-edge AI solutions that will shape the future of our industry. We are seeking a hands\-on individual with a passion for rapid prototyping, a deep curiosity for emerging technologies, and the drive to turn ambitious ideas into reality. If you are eager to build, learn, and grow in the fast\-paced world of Generative and Agentic AI, this role is for you.
Key Responsibilities
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- Develop \& Prototype: Design, build, and iterate on prototypes for Generative and Agentic AI applications with speed and agility, demonstrating the art of the possible.
- Implement AI Models: Implement, train, and fine\-tune a variety of machine learning and deep learning models to solve complex business problems.
- Build Robust Systems: Develop and maintain clean, efficient, and scalable code for AI/ML systems, with a focus on production\-level quality.
- Manage Data Pipelines: Engineer and manage sophisticated data handling and preprocessing pipelines to ensure high\-quality data for training and inference.
- Deploy \& Operate: Utilize MLOps best practices to deploy AI applications in containerized environments like OpenShift, ensuring robust monitoring, scalability, and reliability.
- Innovate \& Research: Actively monitor and research the latest trends, breakthroughs, and tools in AI/ML. Present findings and lead proof\-of\-concept projects to integrate new technologies into our stack.
- Collaborate: Work closely with senior engineers, architects, and product managers in a highly collaborative environment to translate business requirements into technical solutions.
Required Skills and Qualifications
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- Python Proficiency: Strong and efficient programming skills in Python, including deep familiarity with AI\-centric libraries (e.g., NumPy, Pandas, Scikit\-learn).
- ML/DL Foundation: Solid understanding and practical implementation experience with machine learning algorithms and deep learning architectures (e.g., Transformers, CNNs, RNNs).
- Generative AI Experience: Demonstrable understanding of and hands\-on experience with Generative AI, Large Language Models (LLMs), and Agentic AI frameworks.
- Data Expertise: Proven ability in handling and preprocessing large and complex datasets, including data cleaning, feature engineering, and validation.
- MLOps Awareness: Working experience or strong familiarity with MLOps principles, including containerizing applications using Docker and deploying on platforms like OpenShift or Kubernetes.
- Problem\-Solving: Strong analytical and problem\-solving skills with the ability to tackle complex challenges independently.
Core Technical Stack \& Expertise
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The ideal candidate will have hands\-on experience or deep familiarity with the following technologies:
- LLMs: Gemini, OpenAI models (GPT series), Copilot, Claude, Llama, and experience with Local Models.
- Frameworks: LangChain, LlamaIndex, and the Hugging Face ecosystem (Transformers, Datasets, Tokenizers).
- Orchestration: LangGraph and conceptual understanding of building Multi\-Agent Systems.
- Development: Building production\-ready services using Python, FastAPI, and asynchronous programming patterns.
- RAG (Retrieval\-Augmented Generation): Advanced retrieval techniques using Vector DBs (e.g., Pinecone, Chroma) and PostgreSQL (with pgvector).
- ML/DL Platforms: PyTorch and/or TensorFlow for building and fine\-tuning models.
- Deployment \& Monitoring: Containerization with Docker, deploying production APIs, and implementing robust monitoring and logging.
- Applied AI Skills: Advanced Prompt Engineering, AI Workflow Design, and GenAI application optimization for cost, latency, and performance.
The Ideal Candidate
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- You are a builder at heart, driven by a desire to create and experiment.
- You thrive in a fast\-paced environment and excel at turning ideas into functional prototypes quickly.
- You are a voracious learner, constantly keeping up\-to\-date with the latest research papers, open\-source projects, and technology trends in AI.
- You are a proactive and collaborative team player with excellent communication skills.
- You are passionate about solving real\-world problems and delivering tangible impact through technology.
Education:
- Bachelor’s degree/University degree or equivalent experience
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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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Irving Texas United States
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Primary Location Full Time Salary Range:
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$125,760\.00 \- $188,640\.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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*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 $125K-$188K range is below the median 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. This role's midpoint ($157K) sits 13% below the category median. Disclosed range: $125K to $188K.
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
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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