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

Capital One Capital One · Banking · Cambridge, MA +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, governance, and observability. It also requires inventing and introducing LLM optimization techniques to improve performance (scalability, cost, latency, throughput) of large-scale production AI systems. The role leverages AI technologies like AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, and PyTorch.

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
  • Computer Science
  • AI
  • Electrical Engineering
  • Computer Engineering

Nice to have

  • deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
  • designing, developing, integrating, delivering, and supporting complex AI systems
  • leading and mentoring an engineering team
  • influencing cross-functional stakeholders
  • developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang
  • developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
  • staying abreast of the latest AI research and AI systems
  • applying novel techniques in production
  • excellent communication and presentation skills
  • articulating complex AI concepts to peers

What the JD emphasized

  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • governance
  • observability
  • state-of-the-art LLM optimization techniques
  • performance — scalability, cost, latency, throughput

Other signals

  • design, develop, test, deploy, and support AI software components
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • governance
  • observability
  • state-of-the-art LLM optimization techniques
  • performance — scalability, cost, latency, throughput