Senior Lead AI Engineer, Gen AI Platform Services, Agentic AI

Capital One Financial

Confirmed live yesterday Trusted

Quick summary

Work type
On-site
Location
McLean, VASan Francisco, CACambridge, MASan Jose, CANew York, NY
Salary
$229,900–$262,400 / yr
Posted
32 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $209k
This role $246k
$161k most similar roles pay here $273k

This role pays more than 81% of similar roles. Most pay $172,287–$246,150 — the shaded band above. At the midpoint, this role pays about $246k versus about $209k for comparable roles.

Based on 240 similar postings.

Employer

About Capital One Financial

Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking

Capital One Financial currently has 998 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 992 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Senior Lead AI Engineer, Gen AI Platform Services, Agentic AI

Senior Lead AI Engineer - (Gen AI Platform Services, Agentic AI) joins the Intelligent Foundations and Experiences team to develop proprietary solutions that power scalable, responsible AI systems for banking services. This role involves collaborating with cross-functional teams to design, test, deploy, and support critical software components including foundation model training, large language model inference, similarity search, guardrails, and model evaluation. The engineer will implement state-of-the-art LLM optimization techniques to improve performance metrics like latency, throughput, and cost for production systems. Key technologies include PyTorch, Huggingface, VectorDBs, Nemo Guardrails, and AWS Ultraclusters. Candidates must possess expertise in Python, Go, Scala, or Java to build high-performance infrastructure. The role focuses on solving complex technical challenges in the domain of large-scale AI infrastructure to enhance how customers interact with financial products and services.

What you'll do

  • Design, develop, test, and deploy AI software components including foundation model training and LLM inference.
  • Implement similarity search, guardrails, model evaluation, and observability for production AI systems.
  • Develop state-of-the-art LLM optimization techniques to improve performance, scalability, cost, latency, and throughput.
  • Utilize a broad stack of technologies including Huggingface, VectorDBs, PyTorch, and AWS Ultraclusters.
  • Contribute to the technical vision and long-term roadmap of foundational AI systems.
  • Translate complex scientific research into practical, production-ready AI solutions.
  • Lead and mentor engineering teams while influencing cross-functional stakeholders on AI initiatives.

What we're looking for

  • Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 6 years of experience developing AI/ML algorithms.
  • Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 4 years of experience developing AI/ML algorithms.
  • At least 6 years of experience programming with Python, Go, Scala, or Java.
  • Experience deploying scalable and responsible AI solutions on cloud platforms (preferred).
  • Experience designing, developing, integrating, delivering, and supporting complex AI systems (preferred).
  • Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders (preferred).
  • Experience developing AI/ML algorithms like LLM Inference, Similarity Search, and VectorDBs using Python, C++, C#, Java, or Golang (preferred).
  • Experience applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost (preferred).

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