ZipRecruiter

Log In

2

Full Time Mlops Jobs (NOW HIRING)

Lyra Health

Sr. ML Engineer (MLOps)

$143K - $197K/yr

... MLOps), you will be employed by Lyra Health, Inc ... The anticipated annual base salary range for this full-time position is $143,000 to $197,000. The ...

New

TriOptus LLC

Machine Learning Engineer

Atlanta, GA · On-site

$85.92 - $130/hr

* Senior MLOps Engineer (Contractor) About the Role: * Client is seeking an experienced Senior MLOps ... This contract position has potential to transition into a full-time role in the future based on ...

previous pagenext page

Showing results 21-40

All JobsMlops JobsFull Time Mlops Jobs

Full Time Mlops information

See salary details

$12

$17

$25

How much do full time mlops jobs pay per hour?

As of Aug 11, 2026, the average hourly pay for full time mlops in the United States is $17.50, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $18.99 per hour, depending on experience, location, and employer.

What is a full time MLOps?

Full Time MLOps roles focus on building, deploying, and maintaining machine learning models in production environments on a full-time basis. MLOps professionals bridge the gap between data science and IT operations, ensuring that machine learning workflows are reliable, scalable, and automated. Their responsibilities often include managing model versioning, monitoring performance, automating pipelines, and collaborating with both data scientists and engineers. This role is essential for organizations seeking to operationalize AI solutions and maintain them effectively over time.

What are the key skills and qualifications needed to thrive as a full time MLOps engineer, and why are they important?

To thrive as a Full Time MLOps Engineer, you need a solid background in machine learning, software engineering, and cloud computing, often supported by a degree in computer science or a related field. Experience with tools like Docker, Kubernetes, CI/CD pipelines, and cloud platforms (AWS, Azure, GCP), as well as familiarity with version control systems and infrastructure-as-code, is essential. Strong problem-solving, collaboration, and communication skills help you bridge the gap between data science and IT operations teams. These skills ensure the efficient deployment, scalability, and maintenance of machine learning models in production environments.

What are the most common challenges faced by full time MLOps professionals in maintaining production machine learning systems?

Full Time MLOps professionals often encounter challenges like ensuring seamless model deployment, managing version control for both code and data, and monitoring model performance in production environments. They must also address issues related to scalability, reproducibility, and automating workflows to reduce manual intervention. Collaborating closely with data scientists, engineers, and IT teams is essential to troubleshoot issues promptly and implement best practices for continuous integration and delivery.

Is full time MLOps in high demand?

Full-time MLOps roles are in high demand due to the increasing adoption of machine learning and AI across industries. These positions often require skills in cloud platforms, automation, and tools like Docker and Kubernetes, reflecting a growing need for professionals who can deploy and maintain scalable ML systems.

What is the average salary in full time MLOps?

Full-time MLOps engineers typically earn an average salary ranging from $100,000 to $150,000 annually, depending on experience, location, and company size. Salaries can increase with expertise in cloud platforms, automation tools, and machine learning deployment skills.

What is the difference between Full Time Mlops vs Data Engineer?

AspectFull Time MlopsData Engineer
Required CredentialsCertifications in ML, cloud platforms, scriptingCertifications in data warehousing, SQL, cloud platforms
Work EnvironmentCollaborates with data scientists, DevOps teamsWorks with data pipelines, databases, ETL processes
Industry UsageAI/ML projects, deployment pipelinesData infrastructure, data pipeline development

Full Time Mlops roles focus on deploying and maintaining machine learning models in production, requiring knowledge of ML frameworks and cloud services. Data Engineers build and manage data pipelines and infrastructure. While both roles involve working with data and cloud platforms, Full Time Mlops emphasizes ML deployment and automation, whereas Data Engineers concentrate on data architecture and processing.

More about Full Time Mlops jobs
What cities are hiring for Full Time Mlops jobs? Cities with the most Full Time Mlops job openings:
What are the most commonly searched types of Mlops jobs? The most popular types of Mlops jobs are:
What states have the most Full Time Mlops jobs? States with the most job openings for Full Time Mlops jobs include:
What job categories do people searching Full Time Mlops jobs look for? The top searched job categories for Full Time Mlops jobs are:
Full Time Mlops jobs near you
Infographic showing various Full Time Mlops job openings in the United States as of August 2026, with employment types broken down into 92% Full Time, 1% Part Time, and 7% Contract. Highlights an 71% Physical, 10% Hybrid, and 19% Remote job distribution, with an average salary of $36,392 per year, or $17.5 per hour.

Azure Data & MLOps Engineer

Xtreme Solutions Corporate

Petersburg, VA • On-site

$112K - $134K/yr

Full-time

Posted 2 days ago

New

Job description

Description:

CASCOM ESD Enterprise Analytics/AI Program
Schedule: Full-Time / 1.0 FTE
Work Arrangement: Primarily Remote with Required Travel to Fort Lee, VA
Clearance: Active Final Secret Required


The Opportunity

This role sits where data engineering, Azure integration, security, and production ML meet.

The Azure Data & MLOps Engineer will build the Azure data pipelines, integrations, deployment mechanisms, and MLOps foundation supporting enterprise dashboards, applications, and AI/ML models.


What You'll Do
  • Design data-ingestion and system-integration architecture.
  • Connect the analytics environment to authoritative Army systems.
  • Build and maintain production pipelines for ingestion, transformation, reconciliation, and synchronization.
  • Develop REST API, service, database, file-based, and event-driven integrations.
  • Support structured, semi-structured, and document-based data.
  • Establish model-development and deployment environments.
  • Implement CI/CD, versioning, model packaging, releases, rollback, and monitoring.
  • Deploy models as endpoints, containers, batch jobs, or integrated services.
  • Configure identity, secrets, logging, monitoring, alerting, and security controls.
  • Diagnose data latency, data quality, pipeline, model-endpoint, and integration failures.
  • Develop architecture diagrams, interface descriptions, data-flow diagrams, and operational procedures.
  • Support Power BI and Power Apps data connections.

Requirements:Must-Have Qualifications
  • Active final Secret clearance.
  • 6+ years of data engineering, cloud engineering, platform engineering, or software integration experience.
  • 3+ years using Microsoft Azure.
  • Hands-on Azure data-engineering experience.
  • Strong SQL and Python skills.
  • Experience building production ETL/ELT pipelines and API-based integrations.
  • Experience deploying or operationalizing machine-learning models.
  • Experience implementing identity, access, secrets, logging, and monitoring within cloud environments.
  • Production experience with at least two major Azure data technologies, such as:
    • Azure Data Factory
    • Azure Synapse Analytics
    • Azure Databricks
    • Azure SQL
    • Azure Data Lake Storage
    • Microsoft Fabric
  • Experience developing REST API, service, database, file-based, or event-driven integrations.
  • Experience with Git, Azure DevOps, or comparable CI/CD tooling.
  • Experience with data validation, schema management, error handling, logging, and recovery.
  • Bachelor's degree in computer science, information systems, engineering, data engineering, or related discipline; equivalent experience may substitute.
Strongly Preferred
  • Azure Government, cARMY, IL4/IL5/IL6, GCC High, or comparable restricted Government environments.
  • Azure Machine Learning, Azure OpenAI, MLflow, Docker, Kubernetes, or model-serving technologies.
  • Entra ID, managed identities, Key Vault, RBAC, private endpoints, network controls, and secure secrets management.
  • GCSS-Army, SAP, ERP, Army logistics, or DoD system integration.
  • Data lineage, metadata, governance, and architecture documentation.
  • Power BI gateways, semantic models, or Power Platform integration.
  • Classified-data support experience.
About Xtreme SolutionsXSI is a leading provider of information technology and professional services known for outstanding service delivery in a wide range of professional services engagements around the country. Team XSI continually meets and exceeds customer expectations. We are passionate about our work and making a difference. Our vision is to be the best professional services management company for both our customers and our employees. We need employees that share this vision. Our remarkable employees are the key to our company's incredible success. XSI promotes a work environment of trust, integrity, respect, continual improvement, customer satisfaction, and business success. We strive to provide a competitive salary and benefits, an engaging and rewarding work environment, and training and development opportunities.
Equal Employment OpportunityXtreme Solutions, Inc. is an Equal Opportunity Employer and federal contractor. All qualified applicants will receive consideration for employment without discrimination based on any status protected by applicable federal, state, or local law.As a federal contractor, Xtreme Solutions, Inc. takes affirmative action to employ and advance in employment qualified individuals with disabilities and protected veterans. We are committed to providing equal employment opportunities throughout all aspects of employment, including recruitment, hiring, promotion, compensation, training, and other terms and conditions of employment.Equal Opportunity Employer | Individuals with Disabilities | Protected Veterans