Senior Lead AI Engineer (mlx, Agentic Ai, Gen AI Platform Services)

Capital One Capital One · Banking · San Jose, CA +3

Senior Lead AI Engineer role focused on building and scaling Gen AI platform services, including foundation model training, LLM inference, similarity search, guardrails, and model evaluation. The role involves optimizing performance (scalability, cost, latency, throughput) of large-scale production AI systems and contributing to the technical vision for foundational AI systems. Requires strong engineering and AI expertise, with experience in cloud platforms and programming languages like Python.

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 6 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 4 years of experience developing AI and ML algorithms or technologies
  • At least 6 years of experience programming with Python, Go, Scala, or Java

Nice to have

  • 7 years of 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
  • scalable, high-performance AI infrastructure
  • responsible and scalable ways
  • state-of-the-art LLM optimization techniques
  • large scale production AI systems
  • foundational AI systems
  • strong foundation in engineering and mathematics
  • expertise in hardware, software, and AI
  • deploying scalable and responsible AI solutions

Other signals

  • foundation model training
  • large language model inference
  • similarity search
  • guardrails
  • model evaluation
  • experimentation
  • governance
  • observability
  • LLM optimization techniques
  • scalability
  • cost
  • latency
  • throughput
  • foundational AI systems