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Mlops Jobs in Rosenberg, TX (NOW HIRING)

Rysun Labs Inc

Data Technical Architect

Houston, TX · On-site

$70/hr

Exposure to AI/ML and MLOps platforms * Client-facing architecture and consulting experience Qualifications * Bachelor's or Master's degree in Computer Science, IT, Data Science, or related field.

Capgemini

Data Scientist

Houston, TX · On-site

MLOps, model deployment, and monitoring * LangChain and Retrieval-Augmented Generation (RAG) concepts * REST APIs, JSON, Authentication, and Integration patterns * Deployment tools (Azure DevOps, ...

McDermott

Manager Technology (IT)

Houston, TX · On-site

Machine Learning Operations (MLOps) Cloud & Infrastructure * Microsoft Azure * Azure Kubernetes Service (AKS) * Azure Landing Zones * Azure Networking * Azure Storage * Azure Virtual Machines

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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 cities near Rosenberg, TX are hiring for Mlops jobs?

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

Mlops jobs near you
Infographic showing various Mlops job openings in Rosenberg, TX as of August 2026, with employment types broken down into 91% Full Time, 5% Part Time, and 4% Contract. Highlights an 79% Physical, 6% Hybrid, and 15% Remote job distribution.

AIML DevOps Engineer

Robotics Prcocess Automation, LLC

Houston, TX • On-site

$120 - $160/hr

Other

Posted 19 days ago

Job description

We are seeking ahands‑on AIML DevOps Engineerto join our client’s growing AI/ML engineering team in Scottsdale, AZ. The ideal candidate will have strong experience incloud-based MLOps,automation, andDevOpspractices, with the ability to deploy, monitor, and scale AI/ML models efficiently in production environments.

Key Responsibilities
  • Design, build, and maintainML pipelinesfor training, testing, and deploying AI/ML models.
  • Manage and optimizecloud-based ML infrastructureusingGCP Vertex AI,AWS SageMaker, or similar services.
  • Develop and manageCI/CD pipelinesfor AI/ML applications ensuring automated deployment and version control.
  • Implementobservability and monitoringfor model performance, system health, and resource utilization.
  • Automate end-to-end workflows fordata ingestion, model training, deployment, and monitoring.
  • Work cross-functionally withdata scientists, ML engineers, and software developersto ensure scalable and secure operations.
  • ApplyMLOps best practicesfor reproducibility, model governance, and continuous improvement.
Required Skills & Experience
  • 6+ yearsof experience in DevOps / CloudOps / MLOpsroles.
  • Strong experience withGCP AI/ML services(Vertex AI, BigQuery ML, AI Platform) orAWS ML stack(SageMaker, etc.).
  • Hands-on experience withcontainerization and orchestrationtools –Docker, Kubernetes.
  • Proficiency inInfrastructure-as-Codetools –Terraform, CloudFormation, or Deployment Manager.
  • Expertise inCI/CD pipelinesusingJenkins, GitLab CI, GitHub Actions, or ArgoCD.
  • Programming experience inPython, Bash, or Go, including ML frameworks such asTensorFlow, PyTorch, Scikit-learn.
  • Strong understanding of DevOps automation, security, and scalabilityin AI/ML production systems.

ROBOTIC PROCESS AUTOMATION LLC is an equal opportunity employer inclusive of female, minority, disability and veterans, (M/F/D/V). Hiring, promotion, transfer, compensation, benefits, discipline, termination and all other employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity, age, disability, national origin, citizenship/immigration status, veteran status or any other protected status. ROBOTIC PROCESS AUTOMATION LLC will not make any posting or employment decision that does not comply with applicable laws relating to labor and employment, equal opportunity, employment eligibility requirements or related matters. Nor will ROBOTIC PROCESS AUTOMATION LLC require in a posting or otherwise U.S. citizenship or lawful permanent residency in the U.S. as a condition of employment except as necessary to comply with law, regulation, executive order, or federal, state, or local government contract

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