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Mlops Machine Learning Engineer Jobs in Dallas, TX

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and ...

Photon

Machine Learning Engineer - NJ

Addison, TX

$54 - $71.50/hr

We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and ...

USM

JB061761 - MLOps Engineer

Grapevine, TX · On-site

$80/hr

MLOps Engineer Duration: 6 months+, possible extension Rate: $80/hr+, depending on experience ... machine learning systems that power computer vision, RFID integration, and real-time item ...

Vanguard

Manager, Machine Learning Engineer

Dallas, TX

$101K - $133K/yr

Leads a team of Machine Learning Engineers responsible for designing, building, deploying, and scaling AI/ML solutions that support Financial Advisory Services (FAS) business objectives. Partners ...

Paradigm

Senior ML Ops Engineer

Irving, TX · On-site

$140 - $200/hr

Design, build, and maintain scalable MLOps solutions that support the end-to-end machine learning ... Collaborate with Machine Learning Engineers, Data Scientists, Software Engineers, and ...

WTS Paradigm LLC

Senior ML Ops Engineer

Irving, TX · On-site

$123K - $170K/yr

... MLOps solutions that support the end-to-end machine learning lifecycle, including model training ... Engineers, and Infrastructure teams to operationalize ML solutions and improve deployment ...

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$60 - $120K/hr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Compensation ... Work with AWS and SageMaker MLOps ecosystem. Requirements * 6+ years of experience in software ...

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$60 - $120K/hr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Compensation ... Work with AWS and SageMaker MLOps ecosystem. Requirements * 6+ years of experience in software ...

Paradigm

Senior ML Ops Engineer

Irving, TX · Remote

$123K - $170K/yr

... MLOps solutions that support the end-to-end machine learning lifecycle, including model training ... Engineers, and Infrastructure teams to operationalize ML solutions and improve deployment ...

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$60 - $120K/hr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Compensation ... Work with AWS and SageMaker MLOps ecosystem. Requirements * 6+ years of experience in software ...

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$60 - $120K/hr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Compensation ... Work with AWS and SageMaker MLOps ecosystem. Requirements * 6+ years of experience in software ...

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All JobsMlops Machine Learning Engineer JobsMlops Machine Learning Engineer Jobs in Dallas, TX

Mlops Machine Learning Engineer information

See Dallas, TX salary details

$31.2K

$127.4K

$191.4K

How much do mlops machine learning engineer jobs pay per year?

As of Aug 14, 2026, the average yearly pay for mlops machine learning engineer in Dallas, TX is $127,383.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,400.00 and $153,300.00 per year, depending on experience, location, and employer.

What does an MLOps machine learning engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

How does an MLOps machine learning engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

What are the key skills and qualifications needed to thrive as an MLOps machine learning engineer?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.

What are popular job titles related to Mlops Machine Learning Engineer jobs in Dallas, TX?

For Mlops Machine Learning Engineer jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Mlops Machine Learning Engineer jobs in Dallas, TX look for?

The top searched job categories for Mlops Machine Learning Engineer jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Mlops Machine Learning Engineer jobs?

Cities near Dallas, TX with the most Mlops Machine Learning Engineer job openings:

Mlops Machine Learning Engineer jobs near you

Machine Learning Engineer - NJ

Photon

Addison, TX • On-site

$54 - $71.50/hr

Full-time

Re-posted 6 days ago

Job description


Summary:
We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and engineering teams to solve complex business problems, identify data-driven opportunities, and create personalized experiences for customers. You will be responsible for building end-to-end machine learning solutions, implementing models in production, and working with various data frameworks and tools such as Python, Spark, and Databricks.
Key Responsibilities: Analytics Model Development:
  • Analyze use cases and design appropriate analytics models using statistical and machine learning algorithms tailored to specific business requirements.
  • Develop machine learning algorithms to drive personalized customer experiences and provide actionable business insights.
  • Apply expertise in data mining and machine learning techniques, including forecasting, prediction, segmentation, recommendation, and fraud detection.

Data Engineering and Preparation:
  • Extend and augment company data with third-party data to enrich analytics capabilities.
  • Enhance data collection procedures to include necessary information for building analytics systems.
  • Prepare raw data for analysis, including cleaning, imputing missing values, and standardizing data formats using Python data frameworks (e.g., Pandas, NumPy).

Machine Learning Model Implementation:
  • Implement machine learning models, considering both performance and scalability using tools like PySpark in Databricks.
  • Design and build infrastructure to facilitate large-scale data analytics and experimentation.
  • Work with tools like Jupyter Notebooks for data exploration and model development.

What We're Looking For:
  • Educational Background: Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A PhD is preferred but not necessary.
  • Experience:
    • At least 5 years of experience in data analytics, with a strong understanding of core statistical algorithms such as classification and regression analysis.
    • High-level knowledge of analytics use cases such as language analysis, assortment optimization, promotional planning, dynamic pricing, markdown optimization, labor scheduling, and optimization.
  • Technical Skills:
    • Strong experience with Python-based machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
    • Proficiency in using analytics platforms like Databricks for large-scale data processing.
    • At least 4 years of continuous experience with Spark, particularly PySpark implementation.
    • Hands-on experience with data processing and analysis tools such as Pandas, NumPy, and Jupyter Notebooks.

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