Senior Lead Machine Learning Engineer (Intelligent Foundations and Experiences)

Capital One is seeking a Senior Lead Machine Learning Engineer to join their Agile team dedicated to productionizing machine learning applications and systems at scale. The role involves leading teams in building AI/ML capabilities, designing and delivering AI-powered products, and ensuring high availability and performance of machine learning applications.

Responsibilities:

• Lead dedicated pods of software, data and machine learning engineers in building AI/ML capabilities for Credit and Financial Risk Management products, serving as a technical mentor to the team on these core technologies
• Design, build, and deliver AI-powered products and components that solve real-world business problems, leveraging expertise in model experimentation, LLM inference, similarity search, and agentic AI within a collaborative Product and Data Science environment
• Collaborate with a cross-functional team of engineers, data scientists, and designers to develop and scale AI-powered products that enable optimized associate performance and deliver world-class customer value
• Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation)
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Retrain, maintain, and monitor models in production
• Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
• Leverage a broad stack of Open Source and SaaS AI technologies and use programming languages like Python, Scala, or Java