Machine Learning Operations-Engineer II

JOB DESCRIPTION

Why GMF Technology?

Innovation isn't just a talking point at GM Financial, it's how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We're committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry.

Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.

About this role:

You will design, build, and operate cloud-based MLOps capabilities that support the full lifecycle of analytical and generative AI models. This role blends machine learning engineering, data engineering, and software engineering, with a strong focus on automation, scalability, governance, and production readiness. You'll work with technologies such as MLflow, Databricks, Azure Machine Learning, CI/CD pipelines, containerization, and event-driven architectures, partnering closely with data science, IT, and business teams to deliver secure, compliant, and high-impact AI solutions.

RESPONSIBILITIES

What makes you an ideal candidate:

• Build and maintain scalable, cloud-based MLOps platforms supporting 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 model governance, security, and compliance teams to define and enforce MLOps standards
• Collaborate with data science, engineering, and business stakeholders to deliver solutions aligned to business needs
• Research, prototype, and evolve MLOps capabilities to drive innovation and measurable business value

• 2-4 years as Data Scientist or machine learning engineer or similar quantitative field required
• High School Diploma or equivalent required
• Master's Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline preferred,
  
  PhD preferred
• Proven hands-on experience across the full ML/MLOps lifecycle, including MLflow and platforms such as Databricks, Azure ML, or SageMaker
• Experience operationalizing GenAI solutions, including LLM patterns (e.g., RAG), prompt/version management, evaluation, safety, and monitoring
• Strong software and cloud engineering fundamentals, including CI/CD, containerization (Docker), and Kubernetes (AKS)
• Experience with event-driven and streaming architectures and modern cloud-native design patterns
• Advanced skills with Python, SQL, and large-scale data platforms (e.g., Spark, Delta, lakehouse architectures)
• Ability to clearly communicate technical trade-offs and connect AI delivery to business and financial outcomes