Lead Machine Learning Engineer

Capital One Capital One · Banking · Plano, TX

Lead Machine Learning Engineer role focused on productionizing Generative AI and agentic systems at Capital One. Responsibilities include architecting agentic platforms, driving AI evaluation and trust, delivering AI use cases, enforcing enterprise guardrails, translating research into production, and providing technical leadership. Requires experience with GenAI frameworks, vector databases, LLM orchestration, Evals, and operating in regulated environments.

What you'd actually do

  1. Architect Agentic Platforms: Design, develop, and scale core agentic engines and multi-agent workflow solutions, enabling seamless composition of conversational and business automation workflows.
  2. Drive AI Evaluation & Trust: Build and integrate scalable evaluation (Evals) and observability frameworks into solutions to ensure model predictability, performance monitoring, and mitigation of model risk.
  3. Deliver High-Impact Use Cases: 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.
  4. Enforce Enterprise Guardrails: Ensure all AI/ML applications strictly adhere to robust data privacy standards, regulatory postures, and framework auditability/explainability.
  5. Translate Practical Research: 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.

Skills

Required

  • Bachelor's Degree
  • 6 years of experience designing and building data-intensive solutions using distributed computing
  • 4 years of experience programming with Python, Scala, or Java

Nice to have

  • 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)
  • 3+ years of experience with 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

What the JD emphasized

  • productionizing Generative AI
  • agentic systems at scale
  • multi-agent workflows
  • scalable evaluation (Evals)
  • observability frameworks
  • production AI solutions
  • robust data privacy standards
  • regulatory postures
  • framework auditability/explainability
  • LLM optimization
  • retrieval-augmented generation (RAG)
  • multi-agent design patterns
  • strictly regulated environment

Other signals

  • productionizing Generative AI
  • agentic systems at scale
  • multi-agent workflows
  • scalable evaluation (Evals) and observability frameworks
  • deploy production AI solutions
  • strictly adhere to robust data privacy standards, regulatory postures, and framework auditability/explainability
  • LLM optimization, retrieval-augmented generation (RAG), and multi-agent design patterns
  • technical leadership
  • GenAI frameworks (e.g., LangChain, LangGraph, LlamaIndex)
  • Vector Databases
  • LLM or GenAI orchestration systems in production
  • automated evaluations (Evals) and observability pipelines for LLMs
  • deploying AI solutions within a strictly regulated environment