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Flexible Mlops Jobs (NOW HIRING)

NY · On-site

$120 - $160/hr

... Flexible hours with reasonable overlap for team collaboration We are seeking a Senior MLOps Engineer to build and scale machine learning systems in the cloud. This role focuses on automating ML model ...

New

MLOps Engineer Location: San Francisco, CA, USA (Hybrid/Remote) Job Type: Full-Time About the Role ... Flexible Work Schedule * Learning & Certification Support * 401(k) * Career Growth Opportunities ...

Senior MLOps Engineer

$107K - $146K/yr

Position Summary We're hiring a Senior MLOps Engineer with deep machine learning engineering ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

DevOps/MLOps Engineer

Ashburn, VA · On-site

$54 - $74/hr

Flexible Work Hours: Life doesn't always fit into a 9-to-5 schedule. We offer flexibility to help ... Niyam is seeking a DevOps/MLOps Engineer to join our team in support of our work with a federal ...

DevOps/MLOps Engineer

Ashburn, VA · On-site

$54 - $74/hr

Flexible Work Hours : Life doesn't always fit into a 9-to-5 schedule. We offer flexibility to help ... Niyam is seeking a DevOps/MLOps Engineer to join our team in support of our work with a federal ...

DevOps/MLOps Engineer

Ashburn, VA · On-site +1

$54 - $74/hr

Flexible Work Hours: Life doesn't always fit into a 9-to-5 schedule. We offer flexibility to help ... Niyam is seeking a DevOps/MLOps Engineer to join our team in support of our work with a federal ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

Senior Staff MLOps Engineer

Chicago, IL · On-site

$190K - $315K/yr

As a Staff MLOps Engineer, you will build and own the infrastructure, tooling, and scalable systems ... Time Off & Rest Flexible vacation policy. Two company-wide rest weeks per year. * Other Benefits:

ABOUT THE ROLE We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle ... flexible hours that support focus, autonomy, and a healthy work rhythm - Meaningful, modern ...

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Flexible Mlops information

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How much do flexible mlops jobs pay per hour?

As of Aug 7, 2026, the average hourly pay for flexible mlops in the United States is $14.22, according to ZipRecruiter salary data. Most workers in this role earn between $11.78 and $16.11 per hour, depending on experience, location, and employer.

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

To thrive as a Flexible MLOps Engineer, you need a strong background in machine learning, software engineering, and cloud infrastructure, often supported by a degree in computer science or related fields. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications in cloud technologies are highly valuable. Strong problem-solving, adaptability, and collaborative communication help you address evolving project needs and work effectively with data scientists and developers. These skills are crucial for efficiently deploying, maintaining, and scaling machine learning models in dynamic production environments.

What is a Flexible MLOps professional?

A Flexible MLOps professional is someone who manages the deployment, monitoring, and maintenance of machine learning models with the ability to adapt to different tools, platforms, and workflows. This role requires strong knowledge of DevOps practices, cloud services, automation, and machine learning pipelines. Flexibility in this context means being able to work in diverse environments, quickly learn new technologies, and support dynamic project requirements. The goal is to ensure that machine learning solutions are scalable, reliable, and easily maintained across various production settings.

How does a Flexible MLOps role typically interact with data scientists and software engineers during an ML project?

In a Flexible MLOps position, you’ll regularly collaborate with both data scientists and software engineers to streamline the deployment and maintenance of machine learning models. You’ll help data scientists transition their experimental models into robust, production-ready solutions, ensuring scalability and reliability. At the same time, you’ll work with software engineers to integrate ML workflows into broader application architectures, often troubleshooting deployment issues and optimizing pipelines for efficiency. This cross-functional teamwork is essential for delivering successful machine learning products.

What is the difference between Flexible Mlops vs Data Engineer?

AspectFlexible MlopsData Engineer
Required CredentialsCertifications in cloud platforms, scripting, and ML toolsDatabase, programming, and data modeling certifications
Work EnvironmentCloud-based, DevOps pipelines, ML deploymentData warehouses, ETL processes, data pipelines
Employer & Industry UsageTech companies, AI startups, cloud providersFinance, healthcare, tech firms handling large data sets

Flexible Mlops professionals focus on deploying, managing, and scaling machine learning models in cloud environments, often working closely with data scientists. Data Engineers build and maintain data pipelines and infrastructure to support data analysis. While both roles require technical skills and familiarity with cloud and scripting, Flexible Mlops emphasizes ML deployment and automation, whereas Data Engineers concentrate on data architecture and processing.

More about Flexible Mlops jobs
What cities are hiring for Flexible Mlops jobs? Cities with the most Flexible 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 Flexible Mlops jobs? States with the most job openings for Flexible Mlops jobs include:
Infographic showing various Flexible Mlops job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 67% Full Time, 29% Part Time, and 3% Contract. Highlights an 86% Physical, 1% Hybrid, and 13% Remote job distribution, with an average salary of $29,569 per year, or $14.2 per hour.

$120 - $160/hr

Other

Posted 2 days ago

New


Job description

About the Position

The client is focused on improving and scaling machine learning systems. They need a senior MLOps engineer to build end‑to‑end ML pipelines in the cloud, automate model training and deployment, and ensure production ML systems are monitored, reliable, and scalable.

Start: December 1, 2025

Key Responsibilities
  • Automate machine learning model training and deployment processes using CI/CD pipelines
  • Build end-to-end MLOps pipelines in cloud platforms (AWS / GCP / Azure)
  • Implement monitoring and observability for ML models in production environments
  • Optimize infrastructure for ML workloads to improve reliability, scalability, and efficiency
  • Deploy and manage containerized ML applications using Docker and Kubernetes
  • Implement model versioning, experiment tracking, and model registry solutions
  • Set up data pipelines and feature stores for ML model training
  • Ensure ML model performance monitoring, drift detection, and retraining automation
  • Collaborate with data scientists to operationalize ML models from development to production
  • Implement infrastructure as code for ML infrastructure using Terraform or similar tools

Reports to: Client’s Engineering Manager / CTO

Collaborates with: Data Science team, Engineering teams, DevOps team

Technologies

Must-have: MLOps practices, CI/CD for ML (GitHub Actions, GitLab CI, Azure DevOps), Docker, Kubernetes, Cloud platforms (AWS / GCP / Azure), Python, Infrastructure as Code (Terraform), ML frameworks (TensorFlow, PyTorch, scikit-learn), Model deployment (SageMaker, Vertex AI, Azure ML, or Kubeflow)

Nice-to-have: MLflow, Weights & Biases, DVC, Feature stores (Feast, Tecton), Model monitoring (Evidently, WhyLabs), Apache Airflow, Spark, Ray, Helm, ArgoCD, Prometheus, Grafana, Data versioning, A/B testing for models

Soft Skills
  • Fluent English (conversational and written)
  • Strong problem-solving and analytical skills
  • Ability to work independently and implement ML processes end-to-end
  • Collaboration skills working with data scientists and engineers
  • Understanding of ML model lifecycle from data to production
  • Highly self‑managed and able to plan, estimate, and execute tasks
Challenges & Milestones

First 90 Days: Assess current ML infrastructure, implement initial MLOps automation, set up model monitoring for production models

Months 3-6: Build end-to-end ML pipelines with automated training and deployment, implement experiment tracking and model registry, optimize infrastructure costs

Months 6-12: Full MLOps platform operational with automated retraining, drift detection, A/B testing capabilities, and scalable infrastructure supporting multiple ML models

Working Hours

Full-time (40 hours/week), Remote

Flexible hours with reasonable overlap for team collaboration

We are seeking a Senior MLOps Engineer to build and scale machine learning systems in the cloud. This role focuses on automating ML model training, deployment, and monitoring to ensure reliable production ML operations.

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