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Mlops Jobs in Georgetown, TX (NOW HIRING)

Kurai

MLOps Engineer MLOps Engineer

Austin, TX · On-site

$140 - $190/hr

Join our ML Infrastructure team as an MLOps Engineer where you'll build the pipelines and platforms that deploy, monitor, and scale ML models from research to production. You'll bridge the gap ...

Apptronik

Staff MLOps Engineer

Austin, TX · On-site

JOB SUMMARY Apptronik is seeking a Staff MLOps Engineer to own the technical direction of our MLOps platform - the system of record for datasets, experiments, model artifacts, and serving paths that ...

B Capital

Staff MLOps Engineer

Austin, TX · On-site

$120 - $160/hr

Job Summary Apptronik is seeking a Staff MLOps Engineer to own the technical direction of our MLOps platform -- the system of record for datasets, experiments, model artifacts, and serving paths that ...

Apptronik

Staff MLOps Engineer

Austin, TX · On-site

JOB SUMMARY Apptronik is seeking a Staff MLOps Engineer to own the technical direction of our MLOps platform - the system of record for datasets, experiments, model artifacts, and serving paths that ...

AgileEngine

MLOps Engineer ID72409

Austin, 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, LLC.

MLOps Engineer ID72409

Austin, 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

Austin, TX

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

Austin, TX · On-site +1

$93K - $189K/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.

Huntington

MLOps Automation Senior Lead Engineer

Austin, TX · On-site +1

$103K - $135K/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.

Koch Industries

GPU MLOps Engineer

Austin, TX · On-site

$200K - $280K/yr

Your Job The GPU- MLOps Engineer will own the Azure platform layer end-to-end - deploying AI/ML models, provisioning and managing GPU compute, securing the environment, and continuously optimizing ...

Huntington

MLOps Automation Senior Lead Engineer

Austin, TX · On-site +1

$103K - $135K/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.

Rivago infotech inc

Lead ML Engineer

Manor, TX · On-site

$110K - $145K/yr

MLOps, Monitoring, and Production Support * Agentic AI Architecture (good to have) Responsibilities * Build and deploy fraud detection services for production use. * Develop low-latency inference ...

Xforia, Inc.

Site Reliability Engineer

Austin, TX · On-site

$140K/yr

Design and implement cloud solutions, build MLOps on cloud AWS * Data science model containerization, deployment using docker, VLLM, Kubernetes * Communicate with a team of data scientists, data ...

Donato Technologies, Inc

Site Reliability Engineer

Austin, TX · On-site

$56.50 - $75/hr

Design and implement cloud solutions, build MLOps on cloud AWS * Data science model containerization, deployment using docker, VLLM, Kubernetes * Communicate with a team of data scientists, data ...

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Mlops information

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.

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

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

Is MLOps outdated?

MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.

What is the average salary in MLOps?

The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.

What job categories do people searching Mlops jobs in Georgetown, TX look for?

The top searched job categories for Mlops jobs in Georgetown, TX are:

What cities near Georgetown, TX are hiring for Mlops jobs?

Cities near Georgetown, TX with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Georgetown, TX as of August 2026, with employment types broken down into 97% Full Time, 1% Part Time, and 2% Contract. Highlights an 71% Physical, 6% Hybrid, and 23% Remote job distribution.

MLOps Engineer MLOps Engineer

Kurai

Austin, TX • On-site

$140 - $190/hr

Other

Posted 19 days ago

Job description

Join our ML Infrastructure team as an MLOps Engineer where you’ll build the pipelines and platforms that deploy, monitor, and scale ML models from research to production. You’ll bridge the gap between data science and engineering, automating model training, feature engineering, and deployment workflows. Our models serve millions of predictions daily with sub-100ms latency requirements. You’ll work with Kubeflow, MLflow, and custom tooling to make MLOps seamless for our team.

Responsibilities
  • Design and maintain CI/CD pipelines for ML models using GitHub Actions, ArgoCD, and custom tooling
  • Build and operate ML platforms on Kubernetes with GPU acceleration (NVIDIA, AWS EKS)
  • Implement feature stores (Feast) and data versioning (DVC, Delta Lake) for reproducible ML
  • Monitor model performance in production with drift detection, A/B testing, and automated retraining
  • Optimize inference latency through model quantization, ONNX, TensorRT, or custom serving solutions
  • Manage ML experiment tracking with MLflow or Weights & Biases; ensure reproducibility
  • Collaborate with data scientists to productionize research code and establish best practices
  • Implement automated testing for data quality, model validation, and pipeline integrity
Qualifications
  • 4+ years of DevOps/MLOps experience with 2+ years specifically in ML infrastructure
  • Strong Python skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)
  • Production experience with Kubernetes, Docker, and GPU orchestration
  • Deep understanding of ML lifecycle: training, validation, deployment, monitoring, retraining
  • Experience with cloud platforms (AWS SageMaker, GCP Vertex AI, or Azure ML)
  • Familiarity with feature stores, experiment tracking, and ML metadata systems
  • Infrastructure-as-Code skills: Terraform, CloudFormation, or Pulumi
  • Experience with monitoring: Prometheus, Grafana, DataDog, or CloudWatch
  • BS/MS in CS, Engineering, or related field; experience at ML-focused companies is a plus
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