Senior Machine Learning Engineer (Remote Eligible)

- Define and guide the technical strategy and roadmap for our Personalization Platform to enable real-time, tailored product experiences and multi-channel targeted messaging across all Capital One offerings - Collaborate across teams with Product, Data Science, Cloud Infrastructure, and Machine Learning platform groups to align on and jointly develop the advanced recommendation systems and algorithms for our Capital One users - Create and sustain a flexible, scalable rules engine that enables business-driven personalization logic, allowing for dynamic configuration of user segmentation, targeting rules, and real-time decision making while seamlessly integrating with ML-driven recommendations - Architect, build, and maintain robust ML infrastructure and pipelines to support end-to-end workflows, which includes feature extraction, model training, testing, guardrails, model evaluation, deployment, and both real-time and batch inference—ensuring high performance, scalability, and reliability - Design low-latency, event-driven systems to facilitate real-time dynamic personalization and decision-making based on streaming data, user behaviors, and contextual signals - Advance MLOps practices by creating automated, metrics-driven deployment workflows, integration validation, testing systems, and scalable monitoring & observability - Innovate and apply state-of-the-art LLM optimization techniques to enhance performance factors such as scalability, cost, latency, and throughput for large-scale production AI systems - Utilize an extensive array of Open Source and SaaS AI technologies, including AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, and PyTorch - Provide technical leadership to influence architecture, establish engineering standards, direct cross-team strategies, mentor engineers, and drive platform innovation across the organization