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Flexible Mlops Jobs (NOW HIRING)

AgileEngine

MLOps Engineer ID72409

Baltimore, MD · On-site

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 ...

AgileEngine

MLOps Engineer ID72409

Jacksonville, FL · On-site

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 ...

AgileEngine

MLOps Engineer ID72409

Houston, TX · On-site

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 ...

AgileEngine

MLOps Engineer ID72409

Miami, FL · On-site

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 ...

Ernst & Young Advisory Services Sdn Bhd

WI · On-site

$90 - $130/hr

Implement end‑to‑end MLOps/LLMOps: data and model versioning (MLflow/DVC), automated training ... We are proud of our flexible working arrangements, and we will support you to build a successful ...

New

Grindr

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:

NRG

Sr Software Engineer, MLOps

WA · Remote

$150K - $185K/yr

About This Role We are seeking a Sr MLOps Engineer to build the model lifecycle, deployment ... Paid holidays and flexible paid time away Employee/Friends/Family Discounts Medical/dental/vision ...

Huntington

MLOps Automation Senior Lead Engineer

Houston, TX · On-site +1

$99K - $130K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and ... flexible work arrangement. We're combining the best of both worlds: in-office and work from home.

Huntington

MLOps Automation Senior Lead Engineer

Austin, TX · On-site +1

$103K - $135K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and ... flexible work arrangement. We're combining the best of both worlds: in-office and work from home.

Huntington

MLOps Automation Senior Lead Engineer

Chicago, IL · On-site +1

$107K - $140K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and ... flexible work arrangement. We're combining the best of both worlds: in-office and work from home.

Huntington

MLOps Automation Senior Lead Engineer

Austin, TX · On-site +1

$103K - $135K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and ... flexible work arrangement. We're combining the best of both worlds: in-office and work from home.

Huntington

MLOps Automation Senior Lead Engineer

Dallas, TX · On-site +1

$102K - $135K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and ... flexible work arrangement. We're combining the best of both worlds: in-office and work from home.

Huntington

MLOps Automation Senior Lead Engineer

Akron, OH · On-site +1

$99K - $130K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and ... flexible work arrangement. We're combining the best of both worlds: in-office and work from home.

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Flexible Mlops information

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How much do flexible mlops jobs pay per hour?

As of Sep 2, 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 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 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 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.

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Flexible Mlops jobs near you
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 88% Physical, 1% Hybrid, and 11% Remote job distribution, with an average salary of $29,569 per year, or $14.2 per hour.

MLOps Engineer ID72409

AgileEngine

Baltimore, MD • On-site

Full-time

Posted 5 days ago

Job description


AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle transition from AI/ML experimentation to reliable production deployment, building and maintaining the infrastructure, pipelines, and automation needed to deploy models efficiently at scale. You will implement production monitoring systems, drift detection, experiment tracking, and model versioning, while managing cloud environments and GPU compute resources for cost-effective scalability. The role is based onsite in Dallas, TX, and requires close collaboration with data scientists and AI researchers to translate experimental models into production-ready solutions.
WHAT YOU WILL DO
- Own the complete lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment;
- Build, maintain, and scale the infrastructure, automation, and CI/CD workflows necessary for rapid and efficient model deployment;
- Implement robust production monitoring systems, build visibility dashboards, and set up data and concept drift detection to ensure ongoing model accuracy and system reliability;
- Manage experiment tracking and model versioning to ensure full reproducibility and traceability of all models in production;
- Partner closely with data scientists and AI researchers to translate experimental models into robust, production-ready solutions;
- Manage cloud environments and GPU compute resources to ensure systems are not only highly scalable but also cost-effective.
MUST HAVES
- You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;
- 3+ years of professional experience in MLOps, DevOps, Data Engineering, Machine Learning, or Software Engineering;
- Degree in Computer Science, Software Engineering, or a related technical discipline (or equivalent practical experience);
- Engineers located in the US must reside in Dallas, TX, and be willing to work onsite;
- Hands-on experience with experiment tracking, model registry/versioning, drift detection, and production monitoring;
- Strong practical experience navigating cloud environments and managing/provisioning GPU compute resources;
- Deep understanding of containerization (e.g., Docker, Kubernetes) and designing robust CI/CD pipelines for automated deployments;
- A solid conceptual understanding of AI/ML fundamentals to effectively communicate, troubleshoot, and collaborate with applied model developers;
- Upper-intermediate English level.
PERKS AND BENEFITS
- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
- Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
- Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
- Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
- Well-being & support: access local well-being programs and people-focused support tailored to your location

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