GM Financial Technology is looking to modernize operations and reimagine customer interactions by leveraging AI-powered transformation, advanced machine learning, and automation. The company aims to position itself as a leader in digital innovation within the financial industry.
Requirements
- 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
- Experience with messaging technologies such as Azure Event Hubs and Azure Event Grid is highly desirable
- Working knowledge in Azure DevOps or equivalent including GitHub, Boards, CI/CD and other related functionality
- 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
- Demonstrated understanding of applied analytical methodologies including Decision Trees, Neural Networks, Regression, NLP, chat bots and other AI methodologies
Responsibilities
- 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, 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.
- 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
Other
- 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.
- Ability to deliver solutions that are based on a business understanding is essential.
- 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.
- Ability to identify and understand business issues and map these issues into operational and quantitative questions
- Strong written and verbal presentation skills with an ability to communicate effectively with Senior Management by making complex concepts easy to understand
