Senior Lead AI Engineer (genai Platform, Agentic Infrastructure)

Capital One Capital One · Banking · New York, NY +5

Senior Lead AI Engineer role focused on building and scaling GenAI platforms and agentic infrastructure. Responsibilities include designing, developing, and deploying AI software components like foundation model training, LLM inference, similarity search, guardrails, model evaluation, and observability. The role involves optimizing LLM performance for scalability, cost, and latency, leveraging technologies like AWS, Huggingface, VectorDBs, and Nemo Guardrails. It requires strong engineering and AI expertise to deliver AI-powered products and foundational AI systems.

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. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
  3. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
  4. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

Skills

Required

  • Python
  • Go
  • Scala
  • Java
  • AI software components development
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques

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 AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang
  • 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

  • responsible and reliable AI systems
  • responsible and scalable ways
  • responsible AI solutions

Other signals

  • GenAI Platform
  • Agentic Infrastructure
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization