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Mlops Jobs in Arizona (NOW HIRING)

Staffingine LLC

AIML Engineer

Scottsdale, AZ · On-site

Design and implement scalable MLOps supportive data pipelines for data ingestion processing and storage * Experience deploying models with MLOps tools such as Vertex Pipelines Kubeflow or similar ...

Inficare Technologies

AI Python Engineer

Phoenix, AZ · On-site

Contribute to cloud-native ML pipelines and AI application deployment using MLOps, APIs, Docker, and Kubernetes * Collaborate cross-functionally with Security, Risk, Engineering, Governance, and Data ...

Motion Recruitment

IAM Engineer - Phoenix, Az

Phoenix, AZ · On-site

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

This role will involve building and automating MLOps solutions, overseeing experiment management, and ensuring scalable and compliant machine learning lifecycle practices suited to regulatory demands.

IntraEdge

Senior AI/ML & IVR Engineer GCP

Scottsdale, AZ

$105K - $145K/yr

Implement robust MLOps for model monitoring, versioning, CI/CD, retraining, and performance optimization on GCP * Lead technical projects, mentor team members, and ensure best practices in code ...

Arkhya Tech

GenAI Solutions Leader

Phoenix, AZ · On-site

Experience with MLOps/LLMOps tooling and practices (model registry, CI/CD, feature store, prompt/chain/versioning, evaluation, guardrails, monitoring). Strong communication skills with the ability to ...

Early Warning Services

Technical Product Manager - Data

Scottsdale, AZ · Hybrid

$168K - $194K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

The Technical Product Manager collaborates closely with Analytics, MLOps, Engineering team members and other business stakeholders to translate enterprise-wide stakeholder needs into technical model ...

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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.

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

The most popular types of Mlops jobs in Arizona are:

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

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

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

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

What cities in Arizona are hiring for Mlops jobs?

Cities in Arizona with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Arizona as of August 2026, with employment types broken down into 91% Full Time, 3% Part Time, and 6% Contract. Highlights an 71% Physical, 10% Hybrid, and 19% Remote job distribution.

MLOps Engineer

TestingXperts Inc. DBA Damcosoft

Scottsdale, AZ • On-site

Contractor

Re-posted 8 days ago

Job description

Hello,

My name is Sreeja and I represent TestingXperts Inc. TestingXperts is a Specialist QA & Software Testing Company, and an Independent Software Testing division of Damco Group, which is a leading IT Solutions and Services company working with Fortune Enterprises globally. Inheriting the virtues of job quality and optimal user satisfaction from Damco Group, TestingXperts aims at promoting the ethics of connected innovation, thereby seeding the integral values in our employees and achieving unmatched contentment in our clients. To know more about Testingxperts Inc., please visit our website www.testingxperts.com.

If you are interested in the opportunity listed below, please forward your updated resume along with current contact information, or perhaps you can recommend someone who would be interested in this position

Role : MLOps Engineer

Location :  Scottsdale AZ (100% Onsite)

Hire Type : Contract / Full time

No of roles - 7

MLOps Engineer

Role Overview

We are looking for a skilled MLOps Engineer to design, deploy, and manage scalable machine learning pipelines in production. The role focuses on enabling seamless integration of ML models into enterprise systems with reliability, automation, and governance.

Key Responsibilities

  • Design and implement end-to-end ML pipelines from data ingestion to model deployment
  • Build and manage CI/CD pipelines for ML models (training, testing, deployment)
  • Automate model monitoring, retraining, and performance optimization
  • Collaborate with Data Scientists and Data Engineers for productionizing ML models
  • Ensure scalability, reliability, and security of ML systems
  • Manage model versioning, experiment tracking, and lifecycle management
  • Implement best practices for governance, compliance, and reproducibility

Key Skills & Expertise

  • Strong programming skills in Python
  • Experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn
  • Hands-on experience with MLOps tools: MLflow, Kubeflow, Airflow, SageMaker, Azure ML
  • Knowledge of CI/CD tools: Jenkins, GitHub Actions, GitLab CI
  • Experience with cloud platforms: AWS
  • Strong understanding of data pipelines, ETL processes, and distributed systems