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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 Digital S/W Engineer Sr Analyst is a seasoned professional role. Applies in\-depth disciplinary knowledge, contributing to the development of new techniques and the improvement of processes and work\-flow for the area or function. Integrates subject matter and industry expertise within a defined area. Requires in\-depth understanding of how areas collectively integrate within the sub\-function as well as coordinate and contribute to the objectives of the function and overall business. Evaluates moderately complex and variable issues with substantial potential impact, where development of an approach/taking of an action involves weighing various alternatives and balancing potentially conflicting situations using multiple sources of information. Requires good analytical skills in order to filter, prioritize and validate potentially complex and dynamic material from multiple sources. Strong communication and diplomacy skills are required. Regularly assumes informal/formal leadership role within teams. Involved in coaching and training of new recruits Significant impact in terms of project size, geography, etc. by influencing decisions through advice, counsel and/or facilitating services to others in area of specialization. Work and performance of all teams in the area are directly affected by the performance of the individual.
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.
Responsibilities:
- Accountable for executing and driving mid\-size feature application design and development efforts to completion, serving as a development lead on medium\-scale projects and supporting the execution of larger efforts.
- Proficient at operating with considerable autonomy and discretion as you will significantly influence the way an application is designed and developed by providing subject specific expertise and an advanced level of understanding of application programming principles.
- Manage one or more Applications Development teams in an effort to accomplish established goals as well as conduct personnel duties for team (e.g. performance evaluations, hiring and disciplinary actions)
- Utilize in\-depth knowledge and skills across multiple Applications Development areas to provide technical oversight across systems and applications
- Review and analyze proposed technical solutions for projects
- 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.
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:
+ 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.
Recommended Qualifications:
- 5\-8 years in a product development/product management environment
- Strong analytical and quantitative skills; Data driven and results\-oriented
- Experience delivering with an agile methodology
- Experience in affecting large culture change
- Experience leading infrastructure programs
- Skilled at working with third party service providers
- Excellent written and oral communication skills
Education:
- Bachelor’s/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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Digital Software Engineering
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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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$107,120\.00 \- $160,680\.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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Jul 16, 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.*
Salary Context
This $107K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($133K) sits 39% below the category median. Disclosed range: $107K to $160K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Citi AI Hiring
Citi has 9 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, US, Jersey City, NJ, US, Tampa, FL, US. Compensation range: $160K - $500K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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