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Machine Learning Operations Jobs in Houston, TX (NOW HIRING)

Expand Energy

Sr. ML Ops Engineer

Spring, TX · On-site

$96K - $132K/yr

Design, develop, and support machine learning operations (MLOps) platforms and tools in support of data science activities * Implement and maintain automated pipelines supporting the development ...

Beth Page tech

Senior Machine Learning Engineer

Houston, TX · On-site

$99K - $137K/yr

Senior Machine Learning Engineer Location: Houston, TX Environment: Standard, 5-days onsite : Must ... Develop predictive models for trading, risk assessment, and operational efficiency. * Collaborate ...

Expand Energy

Data Architect

Spring, TX

$58.75 - $75.50/hr

The Data Architect defines and evolves platform capabilities that support analytics, data engineering, machine learning operations (MLOps), and emerging AI-driven use cases, ensuring solutions are ...

Hp

AI Data Scientist - Enterprise AI

Spring, TX

$130K - $205K/yr

... operations. This role sits at the intersection of applied research, machine learning engineering, data science, and business transformation. The ideal candidate combines strong technical expertise in ...

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All JobsMachine Learning Operations JobsMachine Learning Operations Jobs in Houston, TX

Machine Learning Operations information

See Houston, TX salary details

$20

$38

$58

How much do machine learning operations jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for machine learning operations in Houston, TX is $38.09, according to ZipRecruiter salary data. Most workers in this role earn between $31.92 and $40.38 per hour, depending on experience, location, and employer.

What are machine learning operations?

Machine Learning Operations (MLOps) is a set of practices that combines machine learning, software engineering, and DevOps to deploy, monitor, and maintain machine learning models in production environments. It involves tasks such as model versioning, automation, testing, and ensuring scalability and reliability using tools like CI/CD pipelines and cloud platforms.

What is the difference between Machine Learning Operations vs Data Scientist?

AspectMachine Learning OperationsData Scientist
Primary FocusDeploying, maintaining, and scaling ML models in productionAnalyzing data to develop insights and build models
Required SkillsML deployment, cloud platforms, automation, scriptingStatistical analysis, data visualization, programming (Python/R)
Work EnvironmentOperations teams, cloud infrastructure, production systemsResearch environments, data analysis teams, R&D
Common CertificationsCloud certifications, MLOps tools certificationsData science certifications, statistical courses

Machine Learning Operations and Data Scientists often collaborate, but MLOps focuses on deploying and maintaining models in production, while Data Scientists focus on analyzing data and developing models. Both roles require technical skills, but their day-to-day tasks and environments differ.

Is machine learning operations a high paying job?

Machine Learning Operations (MLOps) roles typically offer high salaries due to the specialized skills required, such as expertise in cloud platforms, automation, and data engineering. Compensation varies based on experience, location, and company size, but generally ranks above average compared to other tech roles.
What cities near Houston, TX are hiring for Machine Learning Operations jobs? Cities near Houston, TX with the most Machine Learning Operations job openings:
Machine Learning Operations jobs near you
Infographic showing various Machine Learning Operations job openings in Houston, TX as of August 2026, with employment types broken down into 84% Full Time, 13% Part Time, 1% Temporary, and 2% Contract. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution, with an average salary of $79,237 per year, or $38.1 per hour.

Sr. ML Ops Engineer

Expand Energy

Spring, TX • On-site

$96K - $132K/yr

Full-time

Re-posted 24 days ago

Job description

Our core values - Stewardship, Character, Collaborate, Learn, Disrupt - are the lens through which we evaluate every business decision. As a dynamic, growing company that offers extremely competitive compensation and benefits, our employees are our most valued assets and the foundation of Expand's performance among our E&P competitors.

We seek applicants from all backgrounds to ensure we get the best, most creative talent on our team. We realize that, historically, underrepresented groups feel the need to be 100% qualified in order to apply. If you meet any combination of our requirements, we encourage you to apply. We strive to hire people from a wide variety of backgrounds, not just because it's the right thing to do, but because it makes our company stronger.

Job Summary

This senior level position is responsible for building and operating the platforms, pipelines, and standards that support the development, deployment, and lifecycle management of machine learning models and data products. This role is expected to participate and lead communication with business customers as well as cross-functional IT staff, to support business teams delivering machine learning models and advanced analytics solutions.

Job Duties & Responsibilities
  • Collaborate with cross-functional teams including Business Stakeholders, Business Analysts, Data Engineers, and other Software Engineers to identify and define requirements
  • Design, develop, and support machine learning operations (MLOps) platforms and tools in support of data science activities
  • Implement and maintain automated pipelines supporting the development, deployment, and operation of machine learning models and data products, ensuring scalability, reliability, and efficiency
  • Develop and maintain documentation for platforms, pipelines, and operational processes
  • Participate in code reviews, testing, and deployment activities, adhering to SDLC best practices
  • Evaluate and recommend tools, patterns, and process improvements to enhance machine learning and advanced analytics delivery
  • Collaborate with peers to share knowledge, support team capability development, and promote consistent engineering and MLOps practices
Job Specific Skills
  • High proficiency in Python as a primary engineering language, with experience building, testing, and operating production systems supporting machine learning and analytics workloads
  • High proficiency with SQL, and familiarity with Spark or other distributed data processing frameworks
  • Experience establishing and operating a sustainable MLOps environment, including model deployment, pipeline automation, monitoring, and lifecycle management
  • Strong software engineering fundamentals, including object-oriented design, unit testing, exception handling, and use of common design patterns
  • Expertise in data modeling, data warehousing, and ETL/ELT processes supporting analytics and machine learning cases
  • Hands-on experience with cloud-based data platforms and architectures, including Snowflake and Databricks
  • Strong knowledge of CI/CD, DevOps, and release management practices used to deploy and operate production data and machine learning solutions
  • Strong knowledge of SDLC processes, including Agile methodologies
  • Excellent problem-solving skills and ability to troubleshoot complex issues in live production environments
  • Strong communication and collaboration skills, with the ability to work effectively in a team environment
Education

Minimum: High school diploma or GED

Preferred: Bachelor's degree - from accredited university - IT, MIS, Information Systems, Computer Science or related field

Experience

Minimum: 5 - 8 years related work experience

Expand Energy takes necessary action to ensure that all applicants are treated without regard to their race, color, religion, sex, sexual orientation, age, gender identity, national origin, genetic information, disability, pregnancy, military or veteran status or any other protected characteristic as established by law.

Expand Energy Corporation's operations are focused on discovering and developing its large and geographically diverse resource base of unconventional oil and natural gas assets onshore in the United States.

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