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Mlops Manager Jobs in Arizona (NOW HIRING)

Synergie Systems

Junior AI Engineer

Phoenix, AZ · On-site

... MLOps practices for production deployment. · Collaborate with software engineers, data scientists, product managers, and business stakeholders. · Troubleshoot model, application, data, and ...

IntraEdge

Senior AI/ML & IVR Engineer GCP

Scottsdale, AZ · On-site

$105K - $145K/yr

Architect and optimize IVR/Conversational AI systems integrating speech recognition, NLP, and dialog management * Implement robust MLOps for model monitoring, versioning, CI/CD, retraining, and ...

Arkhya Tech

GenAI Solutions Leader

Phoenix, AZ · On-site

... MLOps/LLMOps, GenAI, agentic frameworks, and cloud optimization. Stakeholder Relationship ... Desired Qualifications 5+ years across the AI/ML lifecycle: data management, feature engineering ...

SecondTalent

LLMOps Engineer

Phoenix, AZ · On-site

$160 - $230/hr

MLOps engineers train and deploy custom models. LLMOps engineers operate systems built on top of ... Cloud cost management and FinOps tooling * Monitoring and alerting (Datadog, Grafana, OpenTelemetry ...

Programmers.io

AI/ML Engineer

Scottsdale, AZ · On-site

Manage and optimize cloud-based ML infrastructure (GCP Vertex AI, AWS SageMaker, or equivalent ... Apply MLOps best practices for reproducibility, versioning, and governance of ML models. Required ...

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Mlops Manager information

What is an MLOps manager?

MLOps Managers are professionals responsible for overseeing the deployment, operation, and scaling of machine learning models in production environments. They coordinate teams to ensure seamless collaboration between data scientists, engineers, and IT staff, facilitating the automation of machine learning workflows. Their role involves managing infrastructure, optimizing processes for model monitoring and maintenance, and ensuring compliance with organizational and industry standards. MLOps Managers play a key role in bridging the gap between model development and operationalization, ensuring that machine learning solutions are reliable, reproducible, and scalable.

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

To thrive as an MLOps Manager, you need expertise in machine learning, software engineering, and DevOps practices, often backed by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, Azure, GCP), and certifications such as AWS Certified Machine Learning or Google Cloud Professional ML Engineer are highly beneficial. Strong leadership, problem-solving, and cross-functional communication skills help manage teams and bridge the gap between data science and IT operations. These abilities are crucial for ensuring reliable, scalable, and efficient deployment of machine learning solutions in production environments.

What are some common challenges an MLOps manager faces when integrating machine learning models into production environments?

MLOps Managers often encounter challenges such as ensuring seamless collaboration between data science and engineering teams, managing model versioning, and maintaining reliable deployment pipelines. Balancing rapid experimentation with the need for robust, scalable, and secure production systems can be complex. Additionally, monitoring model performance post-deployment and handling data drift or model degradation are ongoing responsibilities. Effective communication and establishing standardized processes are key to overcoming these challenges and ensuring successful model operations.

What is the difference between Mlops Manager vs Data Scientist?

AspectMlops ManagerData Scientist
Required CredentialsBachelor's/Master's in CS, Engineering, or related; certifications in cloud platforms or MLOps toolsBachelor's/Master's in CS, Statistics, or related; certifications in data analysis or machine learning
Work EnvironmentCollaborates with engineering, DevOps, and data teams to deploy and maintain ML systemsAnalyzes data, builds models, and provides insights to inform business decisions
Employer & Industry UsageTech companies, AI startups, enterprises implementing ML pipelinesResearch institutions, tech firms, finance, healthcare, and marketing sectors

The Mlops Manager focuses on deploying, maintaining, and optimizing machine learning systems within an organization, working closely with engineering and DevOps teams. In contrast, a Data Scientist primarily analyzes data, develops models, and provides insights. While both roles require knowledge of machine learning, the Mlops Manager emphasizes operationalizing ML solutions, whereas the Data Scientist emphasizes data analysis and modeling.

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The most popular types of Mlops jobs in Arizona are:

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Mlops Manager jobs near you

MLOps Engineer

TestingXperts Inc. DBA Damcosoft

Scottsdale, AZ • On-site

Contractor

Re-posted 14 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