Machine Learning Operations-Engineer I

Why GMF Technology?

GM Financial is set to change the auto finance industry and is leading the path of embarking on tech modernization – we have a startup mindset, and preserve our small company culture, in a public company environment with financial stability and intense growth over a decade-plus history. We are data junkies and trust in data and insights to advance our business objectives. We take our goal of zero emission, zero collision, zero congestion, and zero friction very seriously. We believe as an auto finance market leader we are in the driver's seat to lead us in the GM EV mission to change the world. We are building global platforms, in LATAM, Europe, China, U.S. and Canada – and we are looking to grow our high-performing team. GMF is comprised of over 10,000 team members globally. Join our fintech culture within a Blue-Chip company where we are changing the way we use technology to support our customers, dealers and business.

We’re seeking a Machine Learning Ops Engineer to build and scale enterprise-wide MLOps, LLMOps, and GenAIOps capabilities. You’ll develop robust, secure, and automated pipelines that support the full lifecycle of ML/LLM/GenAI models—from development to deployment and monitoring—across a wide range of business applications.

Responsibilities

• Design and implement scalable, reusable ML/LLM/GenAI pipelines.
• Automate model training, validation, deployment, and monitoring processes.
• Ensure production readiness and performance of models, including data drift detection and audit trail creation.
• Collaborate with cross-functional teams (Data Science, IT, Legal, Compliance, etc.) to align solutions with business needs.
• Maintain and improve MLOps standards and practices in partnership with Model Governance.
• Conduct research and proof-of-concept projects to evaluate new tools and techniques.
• Present insights and model performance metrics to stakeholders.
• Support CI/CD pipelines and contribute to test automation and peer reviews.

Qualifications

Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, Analytics, Mathematics, or related field preferred.
• 0–2 years of experience in data science, ML engineering, or a related quantitative role required.
• Experience with full ML/LLM/GenAI lifecycle automation.
• Proficiency in Python and SQL; familiarity with Java, C#, JavaScript is a plus.
• Hands-on experience with Azure ML, Databricks, MLFlow, Docker, Kubernetes, and CI/CD tools (Azure DevOps, GitHub).
• Familiarity with frameworks like TensorFlow, PyTorch, and Keras.
• Strong understanding of data modeling (structured, semi-structured, unstructured).
• Knowledge of cloud platforms (Azure preferred; AWS/GCP acceptable).
• Experience with messaging systems (Azure Event Hubs/Grid), BI tools (Power BI, Synapse, Snowflake), and container orchestration.
• Solid foundation in statistics, probability, and machine learning algorithms.
• Ability to translate business problems into analytical solutions.
• Strong communication skills to present technical concepts to non-technical stakeholders.
• Experience in agile environments (Scrum/Kanban)

What We Offer: Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays.

Our Culture: Our team members define and shape our culture — an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work — we thrive.

Compensation: Competitive pay and bonus eligibility

Work Life Balance: Flexible hybrid work environment, minimum of 2-days a week in office in Irving, TX.

#LI-hybrid