Machine Learning Operations-Engineer II

Job Description

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.

About the Role:

• Apply ML pipelines, Data Science, and Data Engineering practices to design, develop, test, launch, and maintain MLOps/LLMOps/GenAIOps capabilities.
• 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 and data drift; enable automatic audit trails for all artifacts; deploy across a wide range of business applications; sustain a high level of automation across all ML life cycle activities.
• 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 to develop MLOps solutions that meet business requirements across a variety of areas including 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 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.
• Develop and apply algorithms that generate success metrics to improve the value of models/experiments.
• Present findings and analysis for use in decision making and demonstrate bottom‑line financial benefits.
• Collaborate with Cloud Solution Architects to develop solutions.
• Prioritize tasks and meet project deadlines in a fast‑paced work environment.

• Studies and/or experience in full ML/LLM/GenAI lifecycle automation that includes data ingestion, validation, source versioning, attribute lineage, feature engineering, model experimentation, training, validation in release pipelines, responsible AI assessment, model registration, containerized deployment, event-driven monitoring, and integration with ML Flow pipelines.
• Ability to understand and clearly articulate trade‑offs of various approaches to solving machine learning platform problems.
• Experience with messaging technologies such as Azure Event Hubs and Azure Event Grid.
• Working knowledge in Azure Dev Ops or equivalent, including Git Hub, Boards, CI/CD, and related functionality.
• Experience in agile delivery methods like Scrum/Kanban frameworks.
• Broad knowledge in software engineering principles.
• Working experience with large data sets.
• Strong quantitative, analytical and data interpretation skills with a solid foundation of mathematics, probability, and statistics.
• Ability to identify and understand business issues and map these issues into operational and quantitative questions.
• Demonstrated understanding of applied analytical methodologies including Decision Trees, Neural Networks, Regression, NLP, chat bots and other AI methodologies.
• Ability to program in Python and SQL.
• Proficient in Excel, Word, and PowerPoint.
• JavaScript and SAS experience.
• Ability to design and implement model documentation and monitoring protocols.
• Knowledge of analytical databases and data analysis techniques.
• Ability,…