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Mlops Jobs in Utah (NOW HIRING)

Crew Career Center

Senior ML Engineer

Lehi, UT

$98K - $134K/yr

MLOps & Deployment: * Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using services like Vertex AI, GKE, Cloud Functions, and Cloud Run. * Implement robust ...

Patientco

Senior ML Engineer

Lehi, UT

$98K - $134K/yr

MLOps & Deployment: * Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using services like Vertex AI, GKE, Cloud Functions, and Cloud Run. * Implement robust ...

Waystar

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

MLOps & Deployment: * Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using services like Vertex AI, GKE, Cloud Functions, and Cloud Run. * Implement robust ...

BambooHR

Staff Data Scientist

Draper, UT · Hybrid

Establish reusable methodologies, decision-making frameworks, and MLOps practices that become standard across Data Engineering, BI Engineering, and the Knowledge Layer system architecture. * Mentor ...

System One

AI Engineer

Salt Lake City, UT · On-site

Hands-on experience with: o AI/ML and Generative AI o Large Language Models (LLMs) and prompt engineering o RAG architectures and vector databases o MLOps practices * Experience with Docker ...

System One

AI Engineer

Salt Lake City, UT · On-site

Hands-on experience with: o AI/ML and Generative AI o Large Language Models (LLMs) and prompt engineering o RAG architectures and vector databases o MLOps practices * Experience with Docker ...

System One

AI Engineer

Salt Lake City, UT · On-site

Hands-on experience with: o AI/ML and Generative AI o Large Language Models (LLMs) and prompt engineering o RAG architectures and vector databases o MLOps practices * Experience with Docker ...

AMH

Product Engineering Architect

Draper, UT · On-site

Leverages deep expertise in Generative AI, Agentic AI systems, LLM orchestration, and MLOps skillsets to translate complex AI capabilities into production systems and reusable components.

Amh

Product Engineering Architect

Draper, UT

Leverages deep expertise in Generative AI, Agentic AI systems, LLM orchestration, and MLOps skillsets to translate complex AI capabilities into production systems and reusable components.

Berkadia

Senior Engineer - Machine Learning

Midvale, UT · Hybrid

$98K - $135K/yr

Establish and contribute to best practices in MLOps, including model deployment, monitoring, observability, and continuous improvement. * Participate in peer reviews, design discussions, and team ...

Berkadia

Senior Engineer - Machine Learning

Midvale, UT · On-site

$98K - $135K/yr

Establish and contribute to best practices in MLOps, including model deployment, monitoring, observability, and continuous improvement. * Participate in peer reviews, design discussions, and team ...

Socket.dev

Senior Engineer - Machine Learning

Midvale, UT · On-site

$140 - $210/hr

Establish and contribute to best practices in MLOps, including model deployment, monitoring, observability, and continuous improvement. * Participate in peer reviews, design discussions, and team ...

System One

AI Engineer

Salt Lake City, UT · On-site

Hands-on experience with: o AI/ML and Generative AI o Large Language Models (LLMs) and prompt engineering o RAG architectures and vector databases o MLOps practices * Experience with Docker ...

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Mlops information

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.

What are the key skills and qualifications needed to thrive as an MLOps engineer?

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

Is MLOps outdated?

MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.

What is the average salary in MLOps?

The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.

What are the most commonly searched types of Mlops jobs in Utah?

The most popular types of Mlops jobs in Utah are:

What are popular job titles related to Mlops jobs in Utah?

For Mlops jobs in Utah, the most frequently searched job titles are:

What job categories do people searching Mlops jobs in Utah look for?

The top searched job categories for Mlops jobs in Utah are:

What cities in Utah are hiring for Mlops jobs?

Cities in Utah with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Utah as of August 2026, with employment types broken down into 92% Full Time, 6% Part Time, and 2% Contract. Highlights an 79% Physical, 6% Hybrid, and 15% Remote job distribution.

AI Developer - Microsoft Azure & Full Stack Development

Syntricate Technologies

Salt Lake City, UT • On-site

Full-time

Re-posted 12 days ago

Job description

Job Summary:
Syntricate Technologies is seeking a highly skilled and hands-on AI Developer with strong experience in Microsoft Azure cloud services and full-stack application development. The role involves designing, developing, and deploying AI-driven applications that integrate with cloud infrastructure and deliver seamless end-to-end solutions.
Responsibilities:
• Design and implement scalable AI/ML solutions using Azure services (Azure ML, Cognitive Services, Azure Functions, etc.)
• Develop and deploy end-to-end full-stack applications using modern frameworks (React, Angular, .NET Core, Node.js, etc.)
• Integrate AI models into production systems with considerations for scalability, performance, and maintainability
• Collaborate with data scientists, backend engineers, and DevOps teams to operationalize machine learning models
• Automate training, testing, and deployment pipelines using Azure DevOps, CI/CD, and MLOps practices
• Monitor and troubleshoot AI systems in production environments
• Document architectural decisions, data flows, and system interfaces
• Stay up-to-date with emerging trends in AI, machine learning, and cloud technologies
Qualifications:
Required:
• Bachelor's or Master's in Computer Science, Engineering, or a related field
• 3+ years of hands-on AI/ML development experience
• Proficient with Azure cloud services (Azure ML, Data Factory, Blob Storage, Logic Apps, Azure Kubernetes Service)
• Strong programming skills in Python and/or C#, with experience in TensorFlow, PyTorch, or scikit-learn
• Full stack development expertise in both frontend (e.g., React, Angular) and backend (e.g., Node.js, .NET Core) frameworks
• Solid understanding of REST APIs, GraphQL, and microservices architecture
• Experience with DevOps tools (Azure DevOps, GitHub Actions, Docker, Kubernetes)
• Familiarity with MLOps tools and concepts for model lifecycle management
• Excellent problem-solving and communication skills
Preferred:
• Microsoft Certified: Azure AI Engineer Associate or Azure Developer Associate
• Experience with LLMs and generative AI integration (e.g., OpenAI, Azure OpenAI Service)
• Background in data engineering or business intelligence
• Experience with Agile/Scrum methodologies
Company:
Syntricate Technologies offers quality assurance, validation, regulatory, business analysis, and project management services. Founded in 2004, the company is headquartered in Boston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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