Sr. Distinguished AI Engineer

Capital One Capital One · Banking · Cambridge, MA +5

This role focuses 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 engineer will invent and introduce LLM optimization techniques to improve the performance (scalability, cost, latency, throughput) of large-scale production AI systems, leveraging technologies like AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, and PyTorch. The role involves contributing to the technical vision and roadmap of 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

  • Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 10 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies
  • At least 10 years of programming with Python, Go, Scala, or Java

Nice to have

  • 9 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
  • Experience architecting, designing, developing, integrating, delivering, and supporting complex enterprise AI systems
  • Demonstrated ability to lead and mentor an engineering organization and influence cross-functional stakeholders up to the SVP level
  • 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
  • Experience building and deploying multi-modal models for technologies like computer vision, speech recognition, optical character recognition, or other sensor systems in application such as robotics, digital assistants, industrial automation, autonomous driving, or related

What the JD emphasized

  • deliver AI-powered products
  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • state-of-the-art LLM optimization techniques
  • scalability, cost, latency, throughput
  • large scale production AI systems
  • technical vision
  • long term roadmap
  • foundational AI systems
  • deploying scalable and responsible AI solutions
  • complex enterprise AI systems
  • optimizing training and inference software
  • hardware utilization, latency, throughput, and cost
  • staying abreast of the latest AI research
  • judiciously apply novel techniques in production

Other signals

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