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About This Role
Posted Date
4/08/2026
Description
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.
We are seeking a highly skilled and experienced Senior AI/ML \& Agentic AI Engineer with a robust background in Python, Java, database technologies, cloud platforms, and CI/CD DevOps practices to join our Engineering Excellence and Transformation organization. In this critical role, you will be at the forefront of designing, developing, and integrating cutting\-edge AI/ML and Agentic AI solutions, including advanced AI assistant tools, to revolutionize engineering processes and drive significant operational improvements across our enterprise. You will leverage your comprehensive technical expertise to build scalable, resilient, and high\-performance AI systems from conception to deployment, fostering a culture of innovation and continuous delivery.
Key Responsibilities:
- Lead the design, development, and implementation of sophisticated AI/ML, Generative AI, and Agentic AI solutions, including AI assistant tools, in close collaboration with AI architects, product owners, and cross\-functional engineering teams within the Engineering Excellence framework.
- Architect and develop end\-to\-end AI systems, leveraging both Python for machine learning workflows and Java for scalable enterprise\-grade backend services, ensuring seamless integration with existing and new applications.
- Design and implement intelligent agentic systems that exhibit autonomous decision\-making capabilities, enabling advanced automation and self\-optimizing processes.
- Develop, fine\-tune, and optimize Large Language Models (LLMs) using both parameter\-efficient techniques and full fine\-tuning, focusing on their integration into robust, production\-ready systems supported by both Python and Java components.
- Drive the implementation and experimentation with advanced generative AI methods such as prompt engineering and Retrieval\-Augmented Generation (RAG), ensuring their effective deployment and performance in enterprise environments.
- Own the deployment pipeline for AI models and associated services, leveraging CI/CD methodologies, containerization (Docker), and orchestration (Kubernetes) on leading cloud platforms (AWS, Azure, GCP) to ensure secure, scalable, and automated releases.
- Apply deep knowledge of database technologies (SQL and NoSQL) to design efficient data storage, retrieval, and management strategies for AI applications, ensuring data integrity and performance.
- Contribute to the establishment and enforcement of engineering best practices, architectural patterns, and tooling standards for full\-stack AI development, advocating for maintainability, observability, and cost\-efficiency.
- Stay abreast of the latest advancements in AI/ML, Agentic AI, cloud technologies, and DevOps trends, proactively sharing knowledge and driving adoption of innovative solutions.
- Ensure strict adherence to ethical AI guidelines, data privacy regulations, and compliance standards throughout the entire AI solution lifecycle.
- Act as a mentor for junior engineers, providing expert guidance on Python, Java, cloud technologies, and CI/CD best practices, fostering a culture of technical excellence and continuous improvement.
Required Technical Skills:
- Polyglot Programming Expertise:
+ Python: Expert proficiency in Python for AI/ML development, including data manipulation (Pandas), scientific computing (NumPy), and machine learning frameworks.
+ Java: Strong proficiency in Java (e.g., Spring Boot, Microservices, Enterprise Integration Patterns, RESTful APIs) for building scalable, high\-performance, and resilient enterprise applications.
- AI/ML \& Agentic AI:
+ Extensive experience with Generative AI, Agentic AI principles, and the development of AI assistant tools.
+ Hands\-on experience with LLMs and fine\-tuning methods (e.g., LoRA, QLoRA, Adapter/Prefix Tuning, instruction tuning).
+ Practical knowledge of model optimization techniques (e.g., compression, quantization) and familiarity with tools such as DeepSpeed, vLLM, GPTQ, or similar.
+ Proficient in prompt engineering, prompt design tools/frameworks, and building robust RAG systems (hybrid search, multi\-vector retrieval).
+ Proficient with machine learning frameworks (PyTorch, TensorFlow, Keras) and distributed training.
+ Strong skills in Natural Language Processing (NLP) techniques (NER, Dependency Parsing, Text Classification, Topic Modeling), transfer learning, and advanced learning paradigms.
+ Familiarity with generative AI tools and libraries like LangChain, LlamaIndex, Hugging Face, and major GenAI APIs (e.g., OpenAI, Gemini, Claude, AWS Bedrock).
- Database Technologies:
+ Solid experience with both SQL (e.g., PostgreSQL, Oracle, MySQL) and NoSQL (e.g., MongoDB, Cassandra, DynamoDB) databases, including schema design, query optimization, and integration with applications.
- Cloud Platforms:
+ Extensive hands\-on experience with at least one major cloud provider (AWS, Azure, or GCP), including services for compute, storage, networking, AI/ML, and data.
- CI/CD \& DevOps:
+ Strong understanding and practical experience with CI/CD pipelines, automated testing, infrastructure as code (IaC), and configuration management.
+ Expertise with containerization (Docker) and orchestration (Kubernetes, OpenShift).
+ Familiarity with monitoring, logging, and alerting tools for production systems.
- Security \& Compliance:
+ Solid understanding of AI compliance, guardrails, Responsible AI practices, and enterprise security standards within a highly regulated environment.
Required Soft Skills:
- Exceptional collaboration and communication skills, capable of effectively bridging the gap between diverse technical teams (AI/ML, Java, DevOps, Cloud) and non\-technical stakeholders.
- Proactive and analytical problem\-solver, adept at navigating complex technical challenges and driving innovative, cross\-domain solutions in a dynamic environment.
- Ability to clearly articulate complex technical concepts, designs, and solutions to diverse audiences, both technical and non\-technical.
- Strong passion for continuous learning, innovation, and mentoring, contributing significantly to a culture of technical excellence and organizational transformation.
Qualifications:
- At least 6\+ years of progressive experience in software engineering and AI/ML development, with a minimum of 5 years specifically focused on Generative AI, Agentic AI, and full\-stack AI solutions.
- Demonstrated portfolio of successful, impactful projects leveraging Python, Java, cloud services, and CI/CD/DevOps practices in an enterprise setting.
- Extensive experience working with large\-scale distributed systems and architecting solutions for high availability and performance.
Education:
- Bachelor’s or master’s degree in computer science, Data Science, Artificial Intelligence, Software Engineering, or a related quantitative field.
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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:
Jacksonville Florida United States
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Primary Location Full Time Salary Range:
$125 600,00 \- $188 400,00
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 14, 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.*
Salary
125\.00 Hour
Type
Full\-time
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 Information Technology Senior Management Forum, 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.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 44 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Engineering Manager, AI Software Engineer. Positions span McLean, VA, US, Irving, TX, US, Fort Worth, TX, US. Compensation range: $100K - $392K.
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.
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