Lead Machine Learning Engineer

Capital One•Plano, TX

1d•Onsite

About The Position

As a Capital One Lead Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing Generative AI and advanced agentic systems at scale. You’ll lead the detailed technical design, development, and implementation of core agentic architectures and multi-agent workflows using emerging technologies. You’ll focus on system-level architectural design, develop and review complex models and application code, and ensure the high availability, performance, and security of our generative AI applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in generative and agentic machine learning engineering.

Requirements

Bachelor’s Degree
At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
At least 4 years of experience programming with Python, Scala, or Java

Nice To Haves

Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
3+ years of experience with GenAI frameworks (e.g., LangChain, LangGraph, LlamaIndex) and Vector Databases
3 years of experience building, scaling, and optimizing Large Language Model (LLM) or GenAI orchestration systems in production
2+ years of experience building automated evaluations (Evals) and observability pipelines for LLMs
3+ years of on-the-job experience with an industry-recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
Experience deploying AI solutions within a strictly regulated environment, incorporating data privacy and model risk governance
Demonstrated ability to lead technical architecture design and provide deep technical guidance to engineering teams
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
ML industry impact through conference presentations, papers, blog posts, open-source contributions, or patents

Responsibilities

Design, develop, and scale core agentic engines and multi-agent workflow solutions, enabling seamless composition of conversational and business automation workflows.
Build and integrate scalable evaluation (Evals) and observability frameworks into solutions to ensure model predictability, performance monitoring, and mitigation of model risk.
Partner with cross-functional product and business teams to deploy production AI solutions, including next-generation consumer AI experiences, intelligent recommendation engines, and advanced conversational assistants.
Ensure all AI/ML applications strictly adhere to robust data privacy standards, regulatory postures, and framework auditability/explainability.
Stay abreast of practical advancements in LLM optimization, retrieval-augmented generation (RAG), and multi-agent design patterns, judiciously applying these novel techniques to production systems.
Provide technical direction, architectural oversight, and rigorous code reviews for engineering teams, fostering a culture of modern engineering excellence.