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Mlops Jobs in Utah (NOW HIRING)

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 ...

Leash Bio

Machine Learning Engineer

Salt Lake City, UT · On-site

Preferred : • Familiarity with common MLops tooling (e.g., Dagster, Prefect, Airflow, Docker, MLflow, Kubeflow, W&B, Ray, etc.) • Ability to manage own compute cluster • Ability to maximize GPU ...

NRG

Sr Software Engineer, AI Engineer

Lehi, UT · On-site

$115K - $151K/yr

MLOps/DevOps: DVC, CML, GTO, Gitlab Pipelines * Cloud: GCP (Vertex AI/Storage/Cloud Functions), AWS, Azure * Databases: SQL, NoSQL, Vector, Time Series, Graph * Transformers, LLMs, Knowledge Graphs

NRG

Sr Software Engineer, AI Engineer

Lehi, UT · On-site

$115K - $151K/yr

MLOps/DevOps: DVC, CML, GTO, Gitlab Pipelines * Cloud: GCP (Vertex AI/Storage/Cloud Functions), AWS, Azure * Databases: SQL, NoSQL, Vector, Time Series, Graph * Transformers, LLMs, Knowledge Graphs

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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 88% Full Time, 7% Part Time, and 5% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution.

AI Engineer

System One

Salt Lake City, UT • On-site

Contractor

Re-posted 15 hours ago

Job description

Job Title: Junior AI Engineer Location: Salt Lake City, Utah, United States Contract to hire role Description: Junior AI Engineer Position Description Ready to take your career to the next level? CGI is seeking a Junior AI Engineer to design, build, and scale AI-powered product features from concept to production. In this role, you will work as a consultant supporting enterprise clients, solving complex business and technical challenges. You’ll collaborate with a high-performing team, contribute to impactful client solutions, and gain hands-on experience with cutting-edge AI and cloud-native technologies. This position is based onsite at a client location in the Salt Lake City, UT area! Your future duties and responsibilities How you'll make an impact • Design and develop AI-driven product features using ML, GenAI, and LLMs • Build and deploy scalable AI systems using cloud-native architectures • Implement RAG pipelines, vector databases, and conversational AI systems • Develop RESTful APIs and microservices (e.g., FastAPI) for model serving • Containerize and orchestrate applications using Docker and Kubernetes • Ensure system reliability, scalability, security, and cost efficiency • Collaborate cross-functionally with product, engineering, and business teams Required qualifications to be successful in this role What you'll bring • Up to 2 years of experience in engineering or related roles • Familiarity with AI agents and agentic frameworks (e.g., LangChain, LangGraph) • Understanding of agent design patterns and evaluation techniques • Experience with Model Context Protocol (MCP) servers • Proficiency in Python and SQL • 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, Kubernetes, and CI/CD pipelines • Understanding of microservices architecture and API development • Knowledge of serverless design, 12-factor apps, autoscaling, and high availability • Strong problem-solving and communication skills

Ref: #404-IT Pittsburgh

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About System One

Sourced by ZipRecruiter

System One helps employers get work done more efficiently and economically without compromising quality. Over our 35+ year history, we've helped connect thousands of talented people with innovative companies. The excitement of a perfect fit motivates us every single day.

Industry

Business consulting services and recruiting and staffing services

Company size

5,001 - 10,000 Employees

Headquarters location

Pittsburgh, PA, US

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