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About This Role
Zions Bancorporation’s Enterprise Technology and Operations (ETO) team is transforming what it means to work for a financial institution. With a commitment to technology and innovation, we have been providing our community, clients, and colleagues the best experience possible for over 150 years. Help us transform our workforce of the future, today.
We are seeking a dynamic and motivated AI Engineer to join our newly established Innovation Lab. In this role, you will combine core software engineering excellence with advanced AI development to drive the creation of cutting\-edge solutions. You will collaborate with cross\-functional partners to develop AI prototypes and production\-ready applications, ensuring they are built on solid engineering principles. You will contribute to requirements analysis, stakeholder collaboration, and the design, debugging, testing, and deployment of effective, scalable software solutions. Your expertise will help us review, analyze, and improve business processes by leveraging both standard application development and advanced machine learning techniques. This is an exciting opportunity to make a significant impact within the financial sector, contributing to product design, user experience, and ongoing system enhancements.
Visa Sponsorship:
This AI Engineer position is currently not eligible for employment visa sponsorship (e.g., H\-1B visa). This includes, for example, situations where a candidate may have temporary work authorization while enrolled in school or upon graduation (e.g., CPT, OPT) but would need H\-1B visa sponsorship within a few years of employment in order to maintain employment eligibility.
Responsibilities:
Develop innovative AI/ML software solutions, specifically focusing on Generative AI, LLMs, and RAG (Retrieval\-Augmented Generation) architectures, while adhering to enterprise software standards.
Design, build, and maintain robust, scalable RESTful APIs and microservices to expose AI capabilities to internal and external applications using Python (FastAPI/Flask) and/or Java (Spring Boot).
Support testing, configuration management, and source code/change management processes, ensuring strict adherence to Git workflows and version control best practices.
Support and maintain AI pipelines and infrastructure to ensure optimal system performance, scalability, and reliability.
Design, debug, test, and deploy software using Python and/or Java fundamentals, writing clean, maintainable, and efficient code.
Participate in code reviews, ensuring code quality, security, and testability across the team.
Review, analyze, and evaluate business processes, recommending improvements and changes using both standard software automation and AI.
Participate in unit testing, integration testing, feasibility assessments, and systems documentation.
Execute change and release processes (CI/CD), including model versioning and deployment, documenting solutions with clear, well\-commented code.
Build proof of concept (PoC) examples and graphical simulation software.
Participate in product design reviews, offering creative and practical ideas and solutions.
Collaborate with customer service, product managers, developers, and IT to enhance user experience and achieve product goals.
Other duties as assigned.
Qualifications:
2 plus years of experience in software development, database technologies, version control systems, DevOps tools, cloud computing platforms, and microservices architecture. A combination of education and relevant experience may meet qualifications.
Proven experience with Generative AI (prompt engineering, fine\-tuning, RAG) combined with traditional software engineering skills (API design, CI/CD pipelines, Git).
Hand on experience with modern programming languages—specifically Python and/or Java—and application development using various technologies, languages, databases, integrations, frameworks, and systems.
Solid knowledge of software development lifecycles (SDLC), Agile/Scrum methodologies, SQL and database systems, Java/Python programming, and application servers (e.g., WebSphere, Tomcat), data modeling concepts, and integrated application development methodology.
Proven track record in debugging and problem\-solving, with comprehensive experience executing unit, integration, and end\-to\-end testing.
Skilled at translating complex business requirements into strategic programs and aligning them with effective, validated solutions.
Cloud experience: knowledge of modern cloud architectures (GCP preferred, AWS/Azure acceptable) and infrastructure\-as\-code.
Ability to work effectively in a team environment and adapt to changing technology and priorities.
Financial or banking services experience is a plus.
Bachelor’s degree in Computer Science, Computer Engineering, or equivalent required.
Work Location:
This position is fully in office (5 days a week) at the Zions Technology Center \- 7860 South Bingham Junction Blvd, Midvale, UT 84047\.
The Zions Technology Center is a 400,000\-square\-foot technology campus in Midvale, Utah. Located on the former Sharon Steel Mill superfund site, the sustainably built campus is the company’s primary technology and operations center. This modern and environmentally friendly technology center enables Zions to compete for the best technology talent in the state while providing team members with an exceptional work environment with features such as:
Electric vehicle charging stations and close proximity to Historic Gardner Village UTA TRAX station.
At least 75% of the building is powered by on\-site renewable solar energy.
Access to outdoor recreation, parks, trails, shareable bikes, and locker rooms.
Large modern cafe with a healthy and diverse menu.
Healthy indoor environment with ample natural light and fresh air.
LEED\-certified sustainable building that features include the use of low VOC\-emitting construction materials.
Benefits:
Medical, Dental and Vision Insurance \- START DAY ONE!
Life and Disability Insurance, Paid Parental Leave and Adoption Assistance
Health Savings (HSA), Flexible Spending (FSA), and dependent care accounts
Paid Training, Paid Time Off (PTO) and 11 Paid Federal Holidays
401(k) plan with company match, Profit Sharing, competitive compensation in line with work experience
Mental health benefits including coaching and therapy sessions
Tuition Reimbursement for qualifying employees
Employee Ambassador preferred banking products
\#dice
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 Zions Bancorporation, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Zions Bancorporation AI Hiring
Zions Bancorporation has 2 open AI roles right now. They're hiring across AI Engineering Manager, AI/ML Engineer. Based in Midvale, UT, US.
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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