Distinguished AI Engineer

Capital One Capital One · Banking · McLean, VA +4

Distinguished AI Engineer role focused on designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role involves optimizing LLM performance (scalability, cost, latency, throughput) for large-scale production AI systems and contributing to the technical vision and roadmap of foundational AI systems. It requires strong engineering and mathematics foundations, expertise in Python/Go/Scala/Java, and experience with cloud platforms and AI technologies like Huggingface, VectorDBs, 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
  • AI and ML algorithms or technologies

Nice to have

  • deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
  • architecting, designing, developing, integrating, delivering, and supporting complex AI systems
  • leading and mentoring multiple engineering teams
  • influencing cross-functional stakeholders up to the VP level
  • LLM Inference
  • Similarity Search and VectorDBs
  • Guardrails
  • Memory
  • C++
  • C#
  • Golang
  • optimizing training and inference software
  • hardware utilization
  • latency
  • throughput
  • cost
  • staying abreast of the latest AI research and AI systems
  • applying novel techniques in production
  • communication and presentation skills
  • articulate complex AI concepts to peers

What the JD emphasized

  • responsible and reliable AI systems
  • scalable and responsible AI solutions
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • governance
  • observability
  • LLM optimization techniques
  • scalable, high-performance AI infrastructure

Other signals

  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • governance
  • observability
  • LLM optimization techniques
  • scalable, high-performance AI infrastructure