Machine Learning Operations-Engineer II

GM Financial•Irving, TX

9d•Hybrid

About The Position

GM Financial is a global provider of auto finance solutions and the wholly owned captive finance subsidiary of General Motors. The Machine Learning Operations Engineer II will design, build, and operate cloud-based MLOps capabilities supporting the full lifecycle of analytical and generative AI models. The role combines machine learning engineering, data engineering, and software engineering, with an emphasis on automation, scalability, governance, and production readiness. The engineer will work with technologies including MLflow, Databricks, Azure Machine Learning, CI/CD pipelines, containerization, and event-driven architectures, partnering with data science, IT, and business teams to deliver secure, compliant, high-impact AI solutions. The position is hybrid, with two days per week in the office. GM Financial offers competitive pay and bonus eligibility. The position will remain posted until filled. Candidates who require visa sponsorship will not be considered, and agency submissions are not accepted.

Requirements

2–4 years of experience as a data scientist, machine learning engineer, or in a similar quantitative field.
High school diploma or equivalent.
Hands-on experience across the full ML/MLOps lifecycle, including MLflow and platforms such as Databricks, Azure ML, or SageMaker.
Experience operationalizing generative AI solutions, including LLM patterns such as RAG, prompt and version management, evaluation, safety, and monitoring.
Strong software and cloud engineering fundamentals, including CI/CD, Docker, and Kubernetes (AKS).
Experience with event-driven and streaming architectures and modern cloud-native design patterns.
Advanced skills in Python, SQL, and large-scale data platforms such as Spark, Delta, and lakehouse architectures.
Ability to clearly communicate technical trade-offs and connect AI delivery to business and financial outcomes.

Nice To Haves

Master’s degree in Computer Science, Engineering, Analytics, Mathematics, or a related discipline preferred.
PhD preferred.

Responsibilities

Build and maintain scalable, cloud-based MLOps platforms that support analytical and generative AI models end to end.
Develop production-ready machine learning pipelines for training, deployment, monitoring, governance, and lifecycle automation.
Improve the speed, quality, and reliability of model development, experimentation, and operations.
Partner with model governance, security, and compliance teams to define and enforce MLOps standards.
Collaborate with data science, engineering, and business stakeholders to deliver solutions aligned with business needs.
Research, prototype, and evolve MLOps capabilities to drive innovation and measurable business value.