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Machine Learning Operations Jobs in Texas (NOW HIRING)

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$68K - $93K/yr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC, EAD (Only W2, No Sponsorship) Responsibilities * Optimize and maintain large-scale feature ...

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$68K - $93K/yr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC, EAD (Only W2, No Sponsorship) Responsibilities * Optimize and maintain large-scale feature ...

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$68K - $93K/yr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC, EAD (Only W2, No Sponsorship) Responsibilities * Optimize and maintain large-scale feature ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Push the envelope on our operational efficiency by continually refining and advancing our ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior ... Push the envelope on our operational efficiency by continually refining and advancing our ...

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Machine Learning Operations information

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 in Texas are hiring for Machine Learning Operations jobs? Cities in Texas with the most Machine Learning Operations job openings:
Machine Learning Operations jobs near you
Infographic showing various Machine Learning Operations job openings in Texas 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.

Machine Learning Operations Engineer

System One

Dallas, TX • On-site

$68K - $93K/yr

Contractor

Re-posted yesterday

Job description

Job Title: Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC, EAD (Only W2, No Sponsorship)

Responsibilities

  • Optimize and maintain large-scale feature engineering pipelines using PySpark, Pandas, and PyArrow on Hadoop-based infrastructure.
  • Refactor and modularize ML codebases to enhance reusability, maintainability, and performance.
  • Collaborate with platform teams on compute capacity planning, resource allocation, and system upgrades.
  • Integrate with existing model serving frameworks to support testing, deployment, and rollback processes.
  • Monitor and troubleshoot production ML pipelines, ensuring high reliability, low latency, and cost efficiency.
  • Contribute to internal ML platforms by sharing insights, proposing improvements, and documenting best practices.
  • Build near real-time ML pipelines using Kafka and Spark Streaming.
  • Work with AWS and SageMaker MLOps ecosystem.
Requirements
  • 6+ years of experience in software engineering, data engineering, or MLOps roles.
  • Strong programming expertise in Python, with hands-on experience in Pandas, PySpark, and PyArrow.
  • Deep understanding of the Hadoop ecosystem, distributed computing, and performance tuning.
  • Experience with CI/CD pipelines and best practices in ML environments.
  • Hands-on experience with monitoring tools for ML pipeline health and performance.
  • Strong collaboration skills with experience working in cross-functional teams (platform, data science, engineering).
  • Experience contributing to or building internal MLOps frameworks/platforms.
  • Familiarity with SLURM clusters or other distributed job schedulers.
  • Exposure to Kafka, Spark Streaming, or other real-time data processing technologies.
  • Understanding of ML lifecycle management, including versioning, deployment, and drift detection.

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