Senior Lead AI Engineer (ai Foundations, LLM Core and Agentic Ai)

Capital One Capital One · Banking · New York, NY +4

Senior Lead AI Engineer focused on AI Foundations, LLM Core, and Agentic AI. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also includes optimizing LLM performance for scalability, cost, latency, and throughput using various AI technologies and cloud platforms. The role emphasizes building responsible and reliable AI systems for banking applications.

What you'd actually do

  1. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
  2. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
  3. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
  4. Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.

Skills

Required

  • Python
  • Go
  • Scala
  • Java
  • AWS
  • Huggingface
  • VectorDBs
  • Nemo Guardrails
  • PyTorch
  • LLM Inference
  • Similarity Search
  • VectorDBs
  • Guardrails
  • Memory
  • C++
  • C#

Nice to have

  • experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
  • experience designing, developing, integrating, delivering, and supporting complex AI systems
  • demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders
  • experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
  • passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production
  • excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers

What the JD emphasized

  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques
  • scalability
  • cost
  • latency
  • throughput
  • responsible and reliable AI systems
  • state-of-the-art LLM optimization techniques
  • technical vision
  • long term roadmap
  • foundational AI systems

Other signals

  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques
  • scalability
  • cost
  • latency
  • throughput