Sr. ML Ops Engineer

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.