ZipRecruiter

Log In

1

Artificial Intelligence Machine Learning Engineer Jobs in Utah

Varsity Tutors

Machine Learning Tutor

Logan, UT · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Spanish Fork, UT · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Provo, UT · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Paychex

Data Solutions Engineer

Salt Lake City, UT · On-site

$98K - $160K/yr

Overview The Data Solutions Engineer will play a key role in integrating, architecting, and optimizing data systems to support data monetization, analytics, machine learning, artificial intelligence ...

Pwc

NGA AI Engineer Manager

Salt Lake City, UT

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... Artificial Intelligence and Robotics preferred - Demonstrating exceptional team leadership ...

Pwc

NGA AI Engineer Senior Manager

Salt Lake City, UT

$91K - $321K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... Engineering, Artificial Intelligence and Robotics preferred - Crafting and conveying clear ...

Smule

Director of Engineering

Salt Lake City, UT · On-site

Smule is seeking a Director of Engineering to lead teams building scalable, reliable, and AI ... Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related ...

iNovex Information Systems

Data Scientist 2

Ogden, UT · On-site

... machine learning, data science, advanced analytical algorithms, programming, data mining, advanced statistical analysis, advanced mathematical foundations, artificial intelligence, workflow and ...

Whiterabbit.ai

Research Scientist Intern (2025)

Orem, UT · On-site

... with artificial intelligence. We collaborate closely with one of the top medical schools in the ... Learn and understand a large body of research in deep learning and machine learning * Participate ...

next page

Showing results 1-20

All JobsArtificial Intelligence Machine Learning Engineer JobsArtificial Intelligence Machine Learning Engineer Jobs in Utah

Artificial Intelligence Machine Learning Engineer information

See Utah salary details

$28.7K

$117.2K

$176.2K

How much do artificial intelligence machine learning engineer jobs pay per year?

As of Jul 19, 2026, the average yearly pay for artificial intelligence machine learning engineer in Utah is $117,228.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,400.00 and $141,100.00 per year, depending on experience, location, and employer.

What is an Artificial Intelligence Machine Learning Engineer?

An Artificial Intelligence (AI) Machine Learning Engineer is a professional who designs, builds, and implements machine learning models and AI systems. They work with large datasets, develop algorithms, and use programming languages like Python or R to enable computers to learn from data and make predictions or decisions. Their work is essential in fields such as natural language processing, computer vision, and robotics. These engineers collaborate with data scientists, software developers, and business stakeholders to deploy AI solutions in real-world applications.

What are some common challenges faced by Artificial Intelligence Machine Learning Engineers when deploying models to production?

One of the main challenges AI/ML engineers encounter is ensuring that models trained in a controlled environment perform reliably in real-world production settings. This often involves handling issues like data drift, scaling models to handle large volumes of requests, and integrating with existing infrastructure. Collaboration with data engineers and software developers is crucial to streamline deployment, monitor model performance, and address any unexpected behavior quickly. Keeping up with evolving tools and best practices is also important for long-term model maintenance and success.

What is the difference between Artificial Intelligence Machine Learning Engineer vs Data Scientist?

AspectArtificial Intelligence Machine Learning EngineerData Scientist
Required CredentialsBachelor's or higher in CS, AI, ML, or related; certifications like TensorFlow, AWSBachelor's or higher in CS, Statistics, or related; certifications in data analysis or visualization
Work EnvironmentDevelops AI/ML models, coding, deploying algorithms in software environmentsAnalyzes data, builds models, interprets data insights for business decisions
Employer & Industry UsageTech companies, AI startups, R&D departmentsFinance, healthcare, marketing, consulting firms

While both roles involve working with data and algorithms, Artificial Intelligence Machine Learning Engineers focus on designing, building, and deploying AI/ML models in software systems. Data Scientists primarily analyze data to extract insights and support decision-making. The roles often overlap but differ in their core focus and daily tasks.

What are the key skills and qualifications needed to thrive as an Artificial Intelligence Machine Learning Engineer, and why are they important?

To thrive as an Artificial Intelligence Machine Learning Engineer, you need strong programming skills (typically in Python or R), a background in mathematics or statistics, and a degree in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch, or scikit-learn), cloud platforms, and relevant certifications are highly valuable. Problem-solving ability, creativity, and effective communication are important soft skills that distinguish top performers in this role. These competencies are crucial for designing robust AI solutions, collaborating with cross-functional teams, and driving innovation in rapidly evolving technological environments.
What are popular job titles related to Artificial Intelligence Machine Learning Engineer jobs in Utah? For Artificial Intelligence Machine Learning Engineer jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Artificial Intelligence Machine Learning Engineer jobs in Utah look for? The top searched job categories for Artificial Intelligence Machine Learning Engineer jobs in Utah are:
What cities in Utah are hiring for Artificial Intelligence Machine Learning Engineer jobs? Cities in Utah with the most Artificial Intelligence Machine Learning Engineer job openings:
Artificial Intelligence Machine Learning Engineer jobs near you
Infographic showing various Artificial Intelligence Machine Learning Engineer job openings in Utah as of July 2026, with employment types broken down into 79% Full Time, 17% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $117,228 per year, or $56.4 per hour.
AI & Process Automation Engineer

AI & Process Automation Engineer

CCBank

Pleasant Grove, UT • On-site

Full-time

Re-posted 10 days ago

Job description

Summary

Capital Community Bank is seeking a highly motivated AI & Process Automation Engineer to serve as the Bank's first dedicated AI resource. This role is responsible for identifying, designing, building, and deploying AI-driven solutions and lightweight software tools that improve operational efficiency across the organization.

The position will embed directly with business teams, starting in Compliance, then rotating through Operations, Lending, Risk, Finance, and other areas, to discover automation opportunities and deliver measurable results. This role bridges business units, IT, and compliance to ensure AI is implemented responsibly, securely, and effectively.

Key Responsibilities

AI Opportunity Identification & Development

  • Partner with business units across the Bank to identify high-impact problems that can be solved using AI, automation, and intelligent tools.
  • Translate business needs into structured Project Charters that define estimated value, delivery timelines, and measurable success criteria.
  • Design, build, and deploy AI-powered solutions, including process automations, data extraction tools, and workflow optimizations, using both custom code, AI coding tools and existing platforms.
  • Develop lightweight, user-friendly software interfaces that enable non-technical staff to adopt and effectively use deployed solutions.
  • Continuously evaluate and improve deployed solutions based on performance data and user feedback.

AI Governance & Risk Management

  • Maintain an inventory of AI tools, use cases, and data access points across the organization.
  • Ensure all AI implementations comply with internal policies, banking regulations, data privacy requirements, and the Bank's risk management framework.
  • Collaborate with IT, Security, and Compliance to assess risks and approve new AI use cases prior to deployment.
  • Document data flows, system interactions, and AI decision-making processes for audit and regulatory readiness.

Documentation & Process Standardization

  • Create and maintain comprehensive documentation for AI workflows, configurations, and business processes.
  • Develop SOPs, training materials, and user guides for all deployed AI tools.
  • Establish repeatable, scalable processes and frameworks for future AI implementations.

Training & Adoption

  • Provide hands-on training and support to end users on AI tools and best practices.
  • Serve as the Bank's internal subject matter expert on AI, automation, and emerging technology.
  • Drive adoption through education, workshops, and ongoing support; help build an innovation-forward culture.

Performance Tracking & Reporting

  • Define and track KPIs to measure the impact of AI initiatives, including time savings, cost reduction, efficiency gains, and adoption rates.
  • Report on AI usage, adoption, and ROI to senior leadership on a regular cadence.
  • Continuously identify opportunities for improvement, expansion, and new project pipeline development.

Qualifications

Required:

  • Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a closely related field, preferred.
  • Strong programming skills in Python and experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn, or similar).
  • Ability to design and build functional, user-facing software applications (web or desktop) with clean, intuitive interfaces.
  • Experience with generative AI tools and APIs (e.g., OpenAI, Anthropic, open-source LLMs).
  • Strong documentation and process design skills.
  • Understanding of data sensitivity, security, and compliance principles.
  • Excellent communication skills with the ability to translate complex technical concepts into clear, business-friendly language.
  • Self-directed work style with the ability to manage multiple projects and stakeholders simultaneously.
  • Must pass a background check, credit check, and drug screen.

Preferred:

  • Experience in banking, financial services, fintech, or other regulated industries.
  • Familiarity with front-end development frameworks (React, Vue, or similar) for building lightweight internal tools.
  • Working knowledge of SQL, databases, and data pipeline design.
  • Understanding of compliance and regulatory technology concepts (BSA/AML, KYC, TILA, UDAAP, FFIEC guidelines).
  • Familiarity with data governance, risk management, or compliance frameworks.
  • Experience deploying solutions in cloud environments (AWS, Azure, or GCP).
  • Experience with workflow automation tools or scripting (Power Automate, Zapier, etc.).

Key Success Metrics

  • Number and quality of AI solutions successfully deployed and adopted.
  • Time and cost savings generated by deployed tools, as documented in Project Charters.
  • AI adoption rates across departments.
  • Compliance adherence, zero data incidents related to AI usage.
  • Documentation completeness and audit readiness.
  • Stakeholder satisfaction with delivered solutions.

Apply