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Mlops Machine Learning Engineer Jobs in Austin, TX

ExtendMyTeam

Machine Learning Engineer

Austin, TX · On-site

As a Machine Learning Engineer, you will help build and operate production systems that power fraud ... Exposure to MLOps concepts such as CI/CD and model monitoring * Experience working with large ...

Cogent Info

AI/ Machine Learning Engineer

Austin, TX · On-site

MLOps/AI Platform Engineer * AI Application Engineer What You'll Do: * Design, train and deploy ML and GenAI models for real-world applications * Build LLM-based applications, RAG pipelines and AI ...

Lauretta AI LLC

Machine Learning Engineer L-1

Austin, TX · On-site

$80K - $93K/yr

Experience working in MLOps and building pipelines for deploying ML models * Programming skills in Python, C/C++ or Javascript * Understanding of machine learning and deep learning fundamentals

Lauretta AI LLC

Machine Learning Engineer L-1

Austin, TX · On-site

$80K - $93K/yr

Experience working in MLOps and building pipelines for deploying ML models * Programming skills in Python, C/C++ or Javascript * Understanding of machine learning and deep learning fundamentals

1872 Consulting

Machine Learning Engineer

Austin, TX · On-site

Machine Learning Engineer (Austin, TX) Striveworks is a leader in Machine Learning Operations for highly regulated industries such as the Department of Defense/U.S. Military. They enable their ...

Avride

Machine Learning Engineer

Austin, TX · On-site +1

Machine Learning Engineer Austin, TX About the Team Avride develops autonomous vehicle and delivery robot technology, leveraging deep expertise in autonomous systems. With the recent launch of our ...

ExtendMyTeam

Machine Learning Engineer

Austin, TX

Position Summary We are seeking a Machine Learning Engineer to help design, implement, and scale AI-enabled solutions that improve software delivery workflows, automate operational processes, and ...

PayPal

Machine Learning Engineer

Austin, TX · On-site

Machine Learning Engineer This job will validate and develop machine learning models and algorithms to solve complex problems. You will work closely with senior engineers, data scientists, and ...

Q2

Machine Learning Engineer

Austin, TX · On-site

Machine Learning Engineer Q2 is a leading provider of digital banking and lending solutions to banks, credit unions, alternative finance companies, and fintechs in the U.S. and internationally. Our ...

Apple

Machine Learning Engineer

Austin, TX · On-site

Machine Learning Engineer Imagine what you could do here! The people here at Apple don't just create products -- they build the kind of wonder that's revolutionized entire industries. It's the ...

Apple

Machine Learning Engineer

Austin, TX · On-site

We are looking for a passionate, highly motivated, and hands-on applied Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by helping to research and develop ...

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

Mlops Machine Learning Engineer information

See Austin, TX salary details

$31.2K

$127.6K

$191.8K

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

As of May 28, 2026, the average yearly pay for mlops machine learning engineer in Austin, TX is $127,637.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,600.00 and $153,600.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an MLOps Machine Learning Engineer, and why are they important?

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.

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 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.

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 popular job titles related to Mlops Machine Learning Engineer jobs in Austin, TX? For Mlops Machine Learning Engineer jobs in Austin, TX, the most frequently searched job titles are:
What job categories do people searching Mlops Machine Learning Engineer jobs in Austin, TX look for? The top searched job categories for Mlops Machine Learning Engineer jobs in Austin, TX are:
What cities near Austin, TX are hiring for Mlops Machine Learning Engineer jobs? Cities near Austin, TX with the most Mlops Machine Learning Engineer job openings:
Mlops Machine Learning Engineer jobs near you
Infographic showing various Mlops Machine Learning Engineer job openings in Austin, TX as of May 2026, with employment types broken down into 2% Full Time, 89% Part Time, 7% Contract, and 2% Nights. Highlights an 85% Physical, and 15% Remote job distribution, with an average salary of $127,637 per year, or $61.4 per hour.
Machine Learning Engineer (MLOps)

Machine Learning Engineer (MLOps)

Simarn Solutions

Austin, TX • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

About the job Machine Learning Engineer (MLOps)
Job Role: Machine Learning Engineer (MLOps)
Location: Austin, Texas (Onsite)
Type: 1099 Contract | C2H
Job Description:

  • Experienced Machine Learning Engineer with 8-10+ years of hands-on expertise deploying and scaling machine learning models in production environments.
  • Skilled in operationalizing complex models and integrating them into enterprise systems with a focus on performance, scalability, and governance.
  • Partner with data science and engineering teams to deliver, optimize, and maintain production-grade ML models and pipelines.
  • Deploy and manage end-to-end machine learning workflows, from model development to operational monitoring.
  • Proficient in core ML algorithms such as Regression, Classification, and Natural Language Processing (sentiment analysis, topic modeling, TF-IDF).
  • Experienced with tools and frameworks including Scikit-learn, VADER Sentiment, Pandas, and PySpark.
  • Design and maintain dynamic data pipelines tailored to specific use cases.
  • Integrate machine learning solutions within business workflows, ensuring seamless coordination across upstream and downstream systems.
  • Develop and automate reporting pipelines for model performance metrics to support Model Risk Oversight and governance reviews.
  • Create and maintain runbooks for ongoing model support, versioning, and operational maintenance.

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