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About This Role
Job Req Id:
26953775
Location(s):
Jersey City, New Jersey, United States
Job Type:
Hybrid
Posted:
Apr. 10, 2026
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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Overview of the Role:
Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management.
As a bank with a brain and a soul, Citi creates economic value that is systemically responsible and in our clients’ best interests. As a financial institution that touches every region of the world and every sector that shapes your daily life, our Enterprise Operations \& Technology teams are charged with a mission that rivals any large tech company. Our technology solutions are the foundations of everything we do from keeping the bank safe, managing global resources, and providing the technical tools our workers need to be successful to designing our digital architecture and ensuring our platforms provide a first\-class customer experience. We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services.
Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well\-rounded backgrounds who bring their authentic selves to work and complement our culture of delivering results with pride. If you are a problem solver who seeks passion in your work, come join us. We’ll enable growth and progress together.
The GenAI Solutions Architect/Lead Engineer is a senior level position responsible for establishing and implementing new or revised GenAI application systems and programs in coordination with the Technology team. The overall objective of this role is to be highly hands\-on with a capacity to manage the team for applications systems analysis and programming activities.
We are seeking a highly skilled and motivated GenAI Solutions Architect/Lead Engineer to join our dynamic team supporting Finance. This role will focus on developing and implementing cutting\-edge AI/ML solutions, including Generative AI, for various financial applications. The ideal candidate will possess a strong understanding of machine learning algorithms, statistical modeling techniques, and experience working with large datasets. Proven real\-world experience implementing GenAI solutions, particularly those involving Large Language Models (LLMs), Retrieval Augmented Generation (RAG) implementations, and chatbot development for both structured and unstructured data, is essential. Experience with Citi applications and systems is highly desired. This role offers the potential for growth into a leadership position.
Responsibilities:
- Highly Hands\-on GenAI Development: Actively design, develop, and implement complex AI/ML models and algorithms, with a strong focus on Generative AI techniques (e.g., LLMs, GANs, VAEs) for financial applications. This includes hands\-on coding, prototyping, and deploying GenAI solutions.
- RAG Implementation Expertise: Lead the design and implementation of Retrieval Augmented Generation (RAG) systems to enhance the accuracy and contextuality of GenAI outputs, particularly in handling financial data and domain\-specific queries.
- Chatbot Development (Structured \& Unstructured Data): Design, develop, and deploy intelligent chatbot solutions capable of interacting with users across both structured data sources (e.g., databases, APIs) and unstructured data (e.g., documents, emails, free text). This includes leveraging GenAI for natural language understanding and generation in complex conversational flows.
- Document Processing \& OCR Integration: Implement solutions for processing and extracting information from documents, including integrating Optical Character Recognition (OCR) technologies to handle scanned documents and images, making their content available for GenAI models.
- Dynamic SQL Generation using LLM: Develop and deploy solutions leveraging Large Language Models to dynamically generate SQL queries from natural language requests, facilitating data access and analysis from structured databases.
- Python Development \& Ecosystem Mastery: Utilize advanced Python programming skills to build, optimize, and deploy GenAI solutions. This includes extensive experience with relevant Python packages (e.g., PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex, scikit\-learn, pandas, numpy, FastAPI).
- Large Language Model (LLM) Application: Deep practical experience with various LLM architectures, fine\-tuning, prompt engineering, and their application to solve complex financial challenges like natural language understanding, text generation, summarization, and question answering.
- Data Analysis \& Feature Engineering: Analyze large, complex datasets, identify intricate patterns, and extract actionable insights, leveraging GenAI capabilities where appropriate for data augmentation or synthetic data generation.
- Data Pipeline Development: Build and maintain robust, scalable data pipelines for data ingestion, processing, and transformation, specifically optimizing for the unique requirements of GenAI model training and inference.
- Cross\-Functional Collaboration: Partner with multiple management teams and business stakeholders to understand requirements, translate them into technical solutions, and incorporate GenAI possibilities into the discussion, ensuring appropriate integration of functions to meet goals.
- System Enhancements \& Architecture: Identify and define necessary system enhancements to deploy new GenAI products and process improvements. Ensure application design adheres to the overall architecture blueprint, integrating GenAI components seamlessly.
- Problem Resolution: Resolve a variety of high\-impact problems/projects through in\-depth evaluation of complex business processes, system processes, and industry standards, applying GenAI where it can offer innovative solutions.
- Tool \& Technology Evaluation: Evaluate and select appropriate AI/ML tools and technologies, including specialized GenAI frameworks, libraries, and cloud AI services (e.g., AWS SageMaker, Azure ML, Google AI Platform).
- Documentation \& Monitoring: Develop and maintain comprehensive documentation for AI/ML models and systems, specifically addressing GenAI implementations. Manage and monitor the performance, efficiency, and reliability of deployed AI/ML models, including evaluating the effectiveness of GenAI components in production.
- Mentorship \& Leadership: Serve as advisor or coach to mid\-level developers and analysts, allocating work as necessary and fostering a culture of innovation and continuous learning in GenAI.
- Risk Assessment: Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency, especially concerning responsible AI and GenAI ethics.
Qualifications:
- Experience: 6\+ years of relevant experience in Apps Development or systems analysis role, with a significant portion dedicated to AI/ML and demonstrable hands\-on experience in designing, building, and deploying real\-world Generative AI solutions.
- Technical Proficiency:
+ Extensive experience in system analysis and in programming of software applications, with deep expertise in Python for AI/ML development.
+ Proven mastery of Large Language Models (LLMs), including experience with various open\-source and proprietary models, fine\-tuning techniques, and deployment strategies.
+ Hands\-on experience with Retrieval Augmented Generation (RAG) implementations, including vector databases (e.g., Pinecone, FAISS, ChromaDB), embedding models, and efficient retrieval mechanisms.
+ Strong experience in chatbot development, particularly in building conversational AI systems that interact with both structured and unstructured data sources.
+ Experience with document processing, information extraction, and OCR technologies for integrating unstructured data into GenAI workflows.
+ Proficiency in leveraging LLMs for dynamic SQL generation and natural language to code tasks.
+ Proficiency with key Python AI/ML libraries and frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex, scikit\-learn, pandas, and numpy.
+ Experience with MLOps practices, version control (Git), and collaborative development environments.
- Project Management: Experience in managing and implementing successful projects; demonstrated leadership and project management skills within an agile development framework.
- Subject Matter Expertise: Subject Matter Expert (SME) in at least one area of Applications Development or AI/ML domain, with a strong understanding of financial services concepts preferred.
- Adaptability: Ability to adjust priorities quickly as circumstances dictate in a fast\-paced environment.
- Communication: Consistently demonstrates clear and concise written and verbal communication, capable of articulating complex technical concepts to both technical and non\-technical stakeholders.
Education:
- Bachelor’s degree/University degree in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent experience.
- Master’s degree or Ph.D. prefer
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Job Family Group:
Technology
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Job Family:
Digital Software Engineering
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Time Type:
Full time
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Primary Location:
Jersey City New Jersey United States
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Primary Location Full Time Salary Range:
$157 040,00 \- $235 560,00
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.
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Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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Anticipated Posting Close Date:
abr 17, 2026
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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.*
*If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review* *Accessibility at Citi**.*
*View Citi’s* *EEO Policy Statement* *and the* *Know Your Rights* *poster.*
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Citi AI Hiring
Citi has 26 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span Jersey City, NJ, US, New York, NY, US, San Francisco, CA, US. Compensation range: $155K - $300K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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