Senior Lead AI Engineer (fm Hosting, LLM Inference)

Capital One Capital One · Banking · McLean, VA +2

Senior Lead AI Engineer focused on optimizing LLM inference performance, scalability, cost, and latency for production AI systems within Capital One's Intelligent Foundations and Experiences (IFX) team. The role involves designing, developing, and deploying AI software components, including foundation model training, inference, similarity search, guardrails, evaluation, and observability, leveraging cloud platforms and open-source AI technologies.

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 and ML algorithms
  • AI software components development

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
  • LLM Inference
  • Similarity Search and VectorDBs
  • Guardrails
  • Memory
  • C++
  • C#
  • Golang
  • Optimizing training and inference software
  • Hardware utilization
  • Latency
  • Throughput
  • Cost optimization
  • Staying abreast of the latest AI research and AI systems
  • Applying novel techniques in production
  • Communication and presentation skills
  • Articulating complex AI concepts

What the JD emphasized

  • large scale production AI systems
  • LLM optimization techniques
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability

Other signals

  • LLM Inference
  • AI Infrastructure
  • Optimization Techniques