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Senior Mlops Engineer Jobs in Spring, TX (NOW HIRING)

AgileEngine, LLC.

MLOps Engineer ID72409

Houston, TX · On-site

$120 - $180/hr

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 ...

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 transition from AI/ML experimentation to reliable production deployment, building and maintaining the ...

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 transition from AI/ML experimentation to reliable production deployment, building and maintaining the ...

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 deploying MLOps Automation for some of Huntington's most valuable and most challenging data-driven projects.

Expand Energy

Sr. ML Ops Engineer

Spring, TX · On-site

$96K - $132K/yr

Job Summary This senior level position is responsible for building and operating the platforms ... Design, develop, and support machine learning operations (MLOps) platforms and tools in support of ...

Deloitte

Senior Forward Deployed Engineer- AWS

Houston, TX · On-site

$99K - $137K/yr

Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt ... senior stakeholders and engineers to bring measurable business impact from day one. Additional ...

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Senior Mlops Engineer information

See Spring, TX salary details

$52.9K

$112.6K

$163.3K

How much do senior mlops engineer jobs pay per year?

As of Sep 7, 2026, the average yearly pay for senior mlops engineer in Spring, TX is $112,622.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,000.00 and $127,700.00 per year, depending on experience, location, and employer.

What is a senior MLOps engineer?

A Senior MLOps Engineer is an experienced professional who bridges the gap between data science, machine learning, and software engineering. They are responsible for designing, deploying, and maintaining scalable machine learning systems in production environments. Their role involves automating workflows, monitoring model performance, ensuring reproducibility, and managing the infrastructure needed to support machine learning operations. Senior MLOps Engineers also collaborate with data scientists, software developers, and IT teams to ensure smooth integration and continuous delivery of ML models. They play a crucial role in making machine learning solutions reliable, efficient, and scalable for business applications.

What are the key skills and qualifications needed to thrive as a senior MLOps engineer?

To thrive as a Senior MLOps Engineer, you need deep expertise in machine learning workflows, software engineering, and cloud infrastructure, typically supported by a degree in computer science or related fields. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, and platforms such as AWS, GCP, or Azure, as well as certifications in cloud or DevOps, are highly valuable. Strong problem-solving, collaboration, and communication skills set standout professionals apart in this role. These skills and qualities are crucial to ensuring robust, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges senior MLOps engineers face when deploying machine learning models to production environments?

Senior MLOps Engineers often encounter challenges such as managing model versioning, ensuring reproducibility, and scaling deployments across diverse infrastructure. Balancing the needs of data scientists for experimentation with the stability and reliability requirements of production systems can be complex. Additionally, integrating continuous integration and continuous deployment (CI/CD) pipelines for ML workflows and monitoring model performance post-deployment are ongoing responsibilities. Collaboration with data scientists, software engineers, and IT operations is crucial to address these challenges and maintain robust, efficient ML systems.

What is the difference between Senior Mlops Engineer vs Data Scientist?

AspectSenior Mlops EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, Engineering, or related; experience with ML deployment toolsBachelor's/Master's in CS, Statistics, or related; strong programming and statistical skills
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in productionFocus on data analysis, model development, and insights generation
Industry UsageUsed in tech, finance, healthcare for ML deploymentUsed across industries for data analysis and modeling

The main difference is that Senior Mlops Engineers specialize in deploying and maintaining machine learning models in production environments, while Data Scientists focus on developing models and analyzing data. Both roles require strong technical skills, but their day-to-day tasks and focus areas differ significantly.

Are senior MLOps engineers in demand?

Senior MLOps engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are valued for their expertise in deploying, managing, and scaling machine learning models using tools like Kubernetes, Docker, and cloud platforms. The role often requires strong skills in automation, CI/CD pipelines, and cloud infrastructure, making experienced professionals highly sought after.

How much do senior MLOps engineers make?

Senior MLOps engineers typically earn between $120,000 and $180,000 annually, depending on experience, location, and company size. They often have expertise in cloud platforms, automation tools, and machine learning deployment pipelines, which can influence salary levels.

What are popular job titles related to Senior Mlops Engineer jobs in Spring, TX?

For Senior Mlops Engineer jobs in Spring, TX, the most frequently searched job titles are:

What job categories do people searching Senior Mlops Engineer jobs in Spring, TX look for?

The top searched job categories for Senior Mlops Engineer jobs in Spring, TX are:

What cities near Spring, TX are hiring for Senior Mlops Engineer jobs?

Cities near Spring, TX with the most Senior Mlops Engineer job openings:

Senior Mlops Engineer jobs near you
Infographic showing various Senior Mlops Engineer job openings in Spring, TX as of August 2026, with employment types broken down into 92% Full Time, 2% Part Time, and 6% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $112,622 per year, or $54.1 per hour.

MLOps Engineer ID72409

AgileEngine, LLC.

Houston, TX • On-site

$120 - $180/hr

Other

Posted 9 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;
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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