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Mlops Jobs in Rosenberg, TX (NOW HIRING)

AgileEngine, LLC.

MLOps Engineer ID72409

Houston, 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 ...

Huntington

MLOps Automation Senior Lead Engineer

Houston, TX · On-site +1

$99K - $130K/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 Engineer Lead

Houston, TX · On-site

$70K - $140K/yr

Our Enterprise Data and Analytics department is growing, and we're looking for an outstanding MLOps Automation Engineer Lead to join our team. At Huntington, this is an opportunity to work cross ...

Huntington

MLOps Automation Engineer Lead

Houston, TX · On-site

$90K - $118K/yr

Our Enterprise Data and Analytics department is growing, and we're looking for an outstanding MLOps Automation Engineer Lead to join our team. At Huntington, this is an opportunity to work cross ...

Huntington

MLOps Automation Engineer Lead

Houston, TX · On-site

$97K - $128K/yr

Our Enterprise Data and Analytics department is growing, and we're looking for an outstanding MLOps Automation Engineer Lead to join our team. At Huntington, this is an opportunity to work cross ...

Hexagon AB

Principal AI/ML Software Engineer

Houston, TX · On-site

$128K - $172K/yr

Hands-on experience with foundation models (GPT, Claude, Llama), prompt engineering, RAG architectures, and vector databases (Pinecone, Weaviate, Chroma) • MLOps & ModelOps: End-to-end experience ...

Oxy

Advisor IT Systems - AI/ML Ops

Houston, TX · On-site

We are seeking a mid-career MLOps / AI Ops Engineer to support the deployment, monitoring, and lifecycle management of machine learning and advanced analytics solutions across upstream Oil & Gas ...

Oxy

Advisor IT Systems - AI/ML Ops

Houston, TX · On-site

We are seeking a midcareer MLOps / AI Ops Engineer to support the deployment, monitoring, and lifecycle management of machine learning and advanced analytics solutions across upstream Oil & Gas ...

Marencor

Gen AI/ML Solution Architect

Houston, TX · On-site

$60.25 - $79.25/hr

Machine Learning with MLOps * Convert business problems to solutions Job Summary: We are seeking an experienced Gen AI/ML Solution Architect to lead the design, development, and deployment of ...

Hexagon AB

Lead Data Scientist

Houston, TX · On-site

Build end-to-end MLOps pipelines for model training, deployment, monitoring, versioning, and automated retraining with drift detection * Develop predictive models for compliance risk scoring ...

The Avian Consulting LLC

AI Architect

Houston, TX · On-site

MLOps and model lifecycle management * Hands on experience in Azure AI Foundry (Azure Foundry) Experience with QA ecosystems: * Azure DevOps (ADO) * Test automation frameworks (UFT, TOSCA, Selenium ...

IntellectFaces, Inc.

AI/ML Engineer

Houston, TX · On-site

technical Skills Strong expertise in AIML technologies Generative AI LLMs prompt engineering RAG Machine learning model development and deployment NLP predictive analytics MLOps and model lifecycle ...

Cognizant Technology Solutions

Lead AI/ML Developer

Houston, TX · On-site

$56.25 - $73.75/hr

In this role, you will: • Design, develop, deploy, and manage AI/ML, forecasting, predictive analytics, and Generative AI solutions using GCP Vertex AI, Gemini, Claude, and modern MLOps frameworks ...

AMSYS Talent

Azure OpenAI / AI Cloud Engineer

Houston, TX · On-site

$50.25 - $67.25/hr

The ideal candidate will have a strong background in Azure infrastructure , MLOps , and enterprise-grade OpenAI deployments . Key Responsibilities * Design, implement, and maintain Azure AI/ML ...

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All JobsMlops JobsMlops Jobs in Rosenberg, TX

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.

MLOps Engineer ID72409

AgileEngine, LLC.

Houston, TX • On-site

$120 - $180/hr

Other

Posted 9 days ago

Job description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

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 infrastructure, pipelines, and automation needed to deploy models efficiently at scale. You will implement production monitoring systems, drift detection, experiment tracking, and model versioning, while managing cloud environments and GPU compute resources for cost-effective scalability. The role is based onsite in Dallas, TX, and requires close collaboration with data scientists and AI researchers to translate experimental models into production-ready solutions.

WHAT YOU WILL DO
  • - Own the complete lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment;
  • - Build, maintain, and scale the infrastructure, automation, and CI/CD workflows necessary for rapid and efficient model deployment;
  • - Implement robust production monitoring systems, build visibility dashboards, and set up data and concept drift detection to ensure ongoing model accuracy and system reliability;
  • - Manage experiment tracking and model versioning to ensure full reproducibility and traceability of all models in production;
  • - Partner closely with data scientists and AI researchers to translate experimental models into robust, production-ready solutions;
  • - Manage cloud environments and GPU compute resources to ensure systems are not only highly scalable but also cost-effective.
MUST HAVES
  • - You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;
  • - 3+ years of professional experience in MLOps, DevOps, Data Engineering, Machine Learning, or Software Engineering;
  • - Degree in Computer Science, Software Engineering, or a related technical discipline (or equivalent practical experience);
  • - Engineers located in the US must reside in Dallas, TX, and be willing to work onsite;
  • - Hands-on experience with experiment tracking, model registry/versioning, drift detection, and production monitoring;
  • - Strong practical experience navigating cloud environments and managing/provisioning GPU compute resources;
  • - Deep understanding of containerization (e.g., Docker, Kubernetes) and designing robust CI/CD pipelines for automated deployments;
  • - A solid conceptual understanding of AI/ML fundamentals to effectively communicate, troubleshoot, and collaborate with applied model developers;
PERKS AND BENEFITS
  • - Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
  • - Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
  • - Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
  • - Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
  • - Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
  • - Well-being & support: access local well-being programs and people-focused support tailored to your location
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