Machine Learning Operations-Engineer II

Join to apply for the Machine Learning Operations-Engineer II role at GM Financial
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Sponsorship Notice:
At this time, we are unable to offer employment sponsorship for this position. This includes, but is not limited to, H-1B, TN, L1, and OPT visa types.

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.

Apply ML pipelines, Data Science, and Data Engineering practices to design, develop, test, launch, and maintain MLOps/LLMOps/GenAIOps capabilities. This position requires an understanding of the Data Science model development lifecycle, MLFlow architecture, and the benefits of ML development automation and deployment. A general understanding of cloud architectures, software development principles CI/CD, deployment, APIs, microservices, data dev ops, and event-driven cloud architectures is highly desirable.

The ability to deliver solutions that are based on a business understanding is essential. Key responsibilities include enabling ML/GenAI model automation, deployment, scalability, management, robustness, reusability, reproducibility, compliance, and responsible AI. This position requires expertise and passion for working in agile teams to plan effectively, collaborating with broader cross-functional teams, and successfully deliver mission-critical data and analytics projects.

• Develop enterprise-wide and scalable cloud-based MLOps, LLMOps, GenAIOps capabilities that span the full lifecycle of analytical models
• Develop reusable, secure, and robust ML/LLM/GenAI pipelines, monitor model performance, monitor data drift, utilize insights to train models, enable automatic audit trails creation for all artifacts, deploy across a wide range of business applications, and sustain a high level of automation across all ML life cycle activities. This includes developing code and making sure that ML/LLM/GenAI models are production ready
• Continuously improve the speed, quality, and efficiency of model/experiments development, deployment, and maintenance
• Collaborate with Model Management/Governance to develop and maintain enterprise wide MLOps standards
• Collaborate with internal stakeholders and vendors in developing MLOps solutions that meet business requirements across a variety of areas including, but not limited to, Data Science, IT, cybersecurity, compliance, and Legal
• Maintain up to date knowledge about the latest advances in MLOps, engage stakeholders, and champion proactive measures to sustain a cost effective, efficient, and innovative capabilities
• Develop and maintain a deep understanding of business requirements to ensure that MLOps solutions deliver practical and timely value
• Conduct MLOps research and proof of concept projects to improve practice and develop business cases that support business needs
• Develops and apply algorithms that generate success metrics to improve the value of models/experiments.
• Presents findings and analysis for use in decision making and demonstrate bottom-line financial benefits
• Collaborate with Cloud Solution Architects in developing solutions
• Prioritizes tasks and meets project deadlines in a fast-paced work environment

• Studies and/or experience in full ML/LLM/GenAI lifecycle automation that includes data ingestion, data validation, data and source versioning, attribute lineage, feature engineering, evaluation of model experiments, model training, model validation in release pipelines, assessing responsible AI, model registration, containerized deployment, event-driven monitoring, and integration with ML Flow pipelines
• Ability to understand…