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Mlops Data Engineer Jobs in Oklahoma (NOW HIRING)

Expand Energy

Sr. ML Ops Engineer

Oklahoma City, OK · On-site

$85K - $117K/yr

... Engineers to identify and define requirements * Design, develop, and support machine learning ... operations (MLOps) platforms and tools in support of data science activities * Implement and ...

Expand Energy

Sr. ML Ops Engineer

Oklahoma City, OK · On-site

$85K - $117K/yr

... Engineers to identify and define requirements * Design, develop, and support machine learning ... operations (MLOps) platforms and tools in support of data science activities * Implement and ...

Uofl

$185K/yr

Master's degree or higher in Computer Engineering, Computer Science, or Data Science and two (2) ... MLOps & containerized deployment * No-code web UIs & OpenAI-compatible APIs * Open-source release ...

Choctaw Nation of Oklahoma

AI Architect

Durant, OK · On-site

... MLOps standards and best practices to streamline development, automated deployment, and maintain ... data access and regulatory adherence. 5. Optimize compute and cost by making decisions about ...

New

Choctaw Nation of Oklahoma

AI Architect

Durant, OK · On-site

... MLOps standards and best practices to streamline development, automated deployment, and maintain ... data access and regulatory adherence. 5. Optimize compute and cost by making decisions about ...

New

Deloitte

AI Security Engineer Manager

Tulsa, OK · On-site

As a Manager in AI Security Engineering, you will play a critical role in securing the development ... data protection, access control, model validation, and monitoring within DevSecOps and MLOps ...

Accenture

... engineer smarter solutions using AI and data to fundamentally transform business processes and ... Minimum of 3 years of experience with LLMOps/MLOps at scale and multi-cloud architecture (AWS ...

Pwc

ERP AI Engineer - Manager

Oklahoma City, OK

$99K - $232K/yr

In Oracle data and analytics at PwC, you will utilise Oracle's suite of tools and technologies to ... with MLOps tooling and CI/CD pipelines for ML - Experience with vector databases and semantic ...

Pwc

ERP AI Engineer - Manager

Tulsa, OK · On-site

$99K - $232K/yr

In Oracle data and analytics at PwC, you will utilise Oracle's suite of tools and technologies to ... with MLOps tooling and CI/CD pipelines for ML - Experience with vector databases and semantic ...

Cisco

Applied AI Scientist

Oklahoma City, OK · On-site

Collaborate with engineering, product, and data science teams to understand requirements ... MLOps & Continuous Learning - Fluency in automated retraining, drift detection, incremental updates ...

Munich Re

Sr. Solution Architect

Broken Arrow, OK

... management, MLOps, and AI platform integration. * Ensure responsible and compliant AI adoption ... Collaborate with Data Science, Engineering, Security, and Risk teams to enable scalable, secure ...

All JobsMlops Data Engineer JobsMlops Data Engineer Jobs in Oklahoma

Mlops Data Engineer information

What is an MLOps data engineer?

MLOps Data Engineers are professionals who blend expertise in machine learning (ML), operations (Ops), and data engineering to streamline the deployment and management of ML models in production environments. They design and maintain data pipelines, automate workflows, and ensure the scalability, reliability, and reproducibility of machine learning systems. Their role bridges the gap between data scientists and IT operations, enabling seamless integration of ML models into real-world applications.

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

To thrive as an MLOps Data Engineer, you need a strong background in data engineering, machine learning workflows, and software development, usually supported by a degree in computer science or a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), CI/CD pipelines, containerization tools (like Docker and Kubernetes), and familiarity with orchestration frameworks are typically required, along with certifications in cloud or data engineering. Strong problem-solving abilities, collaboration, and clear communication set professionals apart in this role. These skills and qualities are critical to efficiently deploying scalable machine learning solutions and ensuring smooth collaboration between data science and engineering teams.

What are some common challenges MLOps data engineers face when deploying machine learning models into production?

MLOps Data Engineers often encounter challenges such as ensuring seamless integration between data pipelines and model serving infrastructure, managing consistent data quality, and automating model retraining and monitoring. Another common hurdle is maintaining scalability and reliability as data volumes grow, and efficiently collaborating with data scientists, software engineers, and DevOps teams. Addressing these challenges requires strong communication skills, familiarity with cloud platforms, and a proactive approach to troubleshooting and automation.

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

AspectMlops Data EngineerData Scientist
Required SkillsMachine learning deployment, cloud platforms, scripting, data pipelinesStatistical analysis, programming, data visualization, machine learning modeling
CertificationsCloud certifications, ML engineering coursesData science certifications, statistical courses
Work EnvironmentData pipelines, cloud infrastructure, ML deployment systemsData analysis, modeling, research environments
Industry UsageTech companies, AI-focused firms, cloud service providersResearch institutions, analytics firms, tech companies

The main difference between an Mlops Data Engineer and a Data Scientist lies in their focus areas. Mlops Data Engineers specialize in deploying, maintaining, and scaling machine learning models within production environments, emphasizing infrastructure and automation. Data Scientists primarily focus on analyzing data, building models, and deriving insights. Both roles require strong technical skills, but their day-to-day tasks and career paths differ significantly.

Are MLOps Data Engineers in demand?

MLOps Data Engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are skilled in deploying, managing, and maintaining ML models using tools like Docker, Kubernetes, and cloud platforms, making their expertise highly sought after in data-driven organizations.

Is MLOps required for data engineers?

MLOps is increasingly important for data engineers involved in deploying and maintaining machine learning models, as it encompasses practices like automation, monitoring, and version control. While not always mandatory, knowledge of MLOps tools such as Docker, Kubernetes, and CI/CD pipelines enhances a data engineer's ability to support scalable and reliable ML systems.

What are popular job titles related to Mlops Data Engineer jobs in Oklahoma?

For Mlops Data Engineer jobs in Oklahoma, the most frequently searched job titles are:

What job categories do people searching Mlops Data Engineer jobs in Oklahoma look for?

The top searched job categories for Mlops Data Engineer jobs in Oklahoma are:

Mlops Data Engineer jobs near you

Sr. ML Ops Engineer

Expand Energy

Oklahoma City, OK • On-site

$85K - $117K/yr

Full-time

Re-posted 15 days ago

Job description

Our core values - Stewardship, Character, Collaborate, Learn, Disrupt - are the lens through which we evaluate every business decision. As a dynamic, growing company that offers extremely competitive compensation and benefits, our employees are our most valued assets and the foundation of Expand's performance among our E&P competitors.

We seek applicants from all backgrounds to ensure we get the best, most creative talent on our team. We realize that, historically, underrepresented groups feel the need to be 100% qualified in order to apply. If you meet any combination of our requirements, we encourage you to apply. We strive to hire people from a wide variety of backgrounds, not just because it's the right thing to do, but because it makes our company stronger.

Job Summary

This senior level position is responsible for building and operating the platforms, pipelines, and standards that support the development, deployment, and lifecycle management of machine learning models and data products. This role is expected to participate and lead communication with business customers as well as cross-functional IT staff, to support business teams delivering machine learning models and advanced analytics solutions.

Job Duties & Responsibilities
  • Collaborate with cross-functional teams including Business Stakeholders, Business Analysts, Data Engineers, and other Software Engineers to identify and define requirements
  • Design, develop, and support machine learning operations (MLOps) platforms and tools in support of data science activities
  • Implement and maintain automated pipelines supporting the development, deployment, and operation of machine learning models and data products, ensuring scalability, reliability, and efficiency
  • Develop and maintain documentation for platforms, pipelines, and operational processes
  • Participate in code reviews, testing, and deployment activities, adhering to SDLC best practices
  • Evaluate and recommend tools, patterns, and process improvements to enhance machine learning and advanced analytics delivery
  • Collaborate with peers to share knowledge, support team capability development, and promote consistent engineering and MLOps practices
Job Specific Skills
  • High proficiency in Python as a primary engineering language, with experience building, testing, and operating production systems supporting machine learning and analytics workloads
  • High proficiency with SQL, and familiarity with Spark or other distributed data processing frameworks
  • Experience establishing and operating a sustainable MLOps environment, including model deployment, pipeline automation, monitoring, and lifecycle management
  • Strong software engineering fundamentals, including object-oriented design, unit testing, exception handling, and use of common design patterns
  • Expertise in data modeling, data warehousing, and ETL/ELT processes supporting analytics and machine learning cases
  • Hands-on experience with cloud-based data platforms and architectures, including Snowflake and Databricks
  • Strong knowledge of CI/CD, DevOps, and release management practices used to deploy and operate production data and machine learning solutions
  • Strong knowledge of SDLC processes, including Agile methodologies
  • Excellent problem-solving skills and ability to troubleshoot complex issues in live production environments
  • Strong communication and collaboration skills, with the ability to work effectively in a team environment
Education

Minimum: High school diploma or GED

Preferred: Bachelor's degree - from accredited university - IT, MIS, Information Systems, Computer Science or related field

Experience

Minimum: 5 - 8 years related work experience

Expand Energy takes necessary action to ensure that all applicants are treated without regard to their race, color, religion, sex, sexual orientation, age, gender identity, national origin, genetic information, disability, pregnancy, military or veteran status or any other protected characteristic as established by law.

Expand Energy Corporation's operations are focused on discovering and developing its large and geographically diverse resource base of unconventional oil and natural gas assets onshore in the United States.

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