Cloud MLOps Engineer II — GenAI Platform

GM Financial; Irving (TX); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

401K matching

Bonding leave for new parents (12 wks,

Tuition assistance

GM employee auto discount

Community service pay

Nine company holidays

Job summary

GM Financial seeks an experienced ML/MLOps engineer to design, build, and operate cloud-based MLOps capabilities for the full lifecycle of analytical and GenAI models. You’ll leverage MLflow, Databricks and Azure ML, with CI/CD, containers, and event-driven patterns to deliver secure, compliant AI solutions.

This role blends ML engineering, data engineering, and software engineering, focusing on automation, scalability, and production readiness in a hybrid work environment.

Qualifications

• 2-4 years in data science, ML engineering, or similar quantitative field.
• Master's degree preferred in CS/Engineering/Analytics; PhD preferred.
• Proven hands-on experience across full ML/MLOps lifecycle including MLflow and cloud platforms.

Responsibilities

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

Skills

MLOps

Python

SQL

CI/CD

Docker

Kubernetes

Spark

Delta Lake

GenAI

LLM patterns

Education

Master's Degree in CS/Engineering/Analytics

PhD preferred

High School Diploma

Tools

MLflow

Databricks

Azure ML

SageMaker

Job description

GM Financial seeks an experienced ML/MLOps engineer to design, build, and operate cloud-based MLOps capabilities for the full lifecycle of analytical and GenAI models. You’ll leverage MLflow, Databricks and Azure ML, with CI/CD, containers, and event-driven patterns to deliver secure, compliant AI solutions.

This role blends ML engineering, data engineering, and software engineering, focusing on automation, scalability, and production readiness in a hybrid work environment.