Lead AI Engineer (Gen AI Platform Services: Agentic AI, Agent Guardrails, Agent Evaluation, Agent Memory)

Capital One Financial

Confirmed live yesterday Trusted

Quick summary

Work type
On-site
Location
San Francisco, CAMcLean, VACambridge, MASan Jose, CANew York, NY
Salary
$197,300–$225,100 / yr
Posted
14 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $204k
This role $211k
$151k most similar roles pay here $262k

This role pays more than 57% of similar roles. Most pay $162,000–$246,150 — the shaded band above. At the midpoint, this role pays about $211k versus about $204k 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.

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View all roles at Capital One Financial

At a glance

TL;DR · Lead AI Engineer (Gen AI Platform Services: Agentic AI, Agent Guardrails, Agent Evaluation, Agent Memory)

Lead AI Engineer (Gen AI Platform Services: Agentic AI, Agent Guardrails, Agent Evaluation, Agent Memory) joins the Intelligent Foundations and Experiences team to develop and deploy proprietary solutions for banking services. This role involves collaborating with cross-functional teams of engineers, research scientists, and product managers to build AI-powered products that improve internal workflows and customer interactions. The engineer will design, test, and support software components including foundation model training, large language model inference, similarity search, guardrails, evaluation, and observability. Key technical requirements include proficiency in Python, Go, Scala, or Java, along with experience using PyTorch, Huggingface, VectorDBs, Nemo Guardrails, and AWS Ultraclusters. The role focuses on implementing state-of-the-art LLM optimization techniques to improve performance metrics like scalability, cost, latency, and throughput while managing the technical roadmap for foundational AI systems in a production environment.

What you'll do

  • Design, develop, test, and deploy AI software components including foundation model training and LLM inference.
  • Implement agentic AI features such as guardrails, evaluation systems, and memory components.
  • Develop infrastructure for similarity search using VectorDBs and other open-source AI technologies.
  • Apply state-of-the-art optimization techniques to improve performance, cost, latency, and throughput of production systems.
  • Contribute to the technical vision and long-term roadmap of foundational AI systems.
  • Translate scientific research into practical, scalable production features for banking products.
  • Manage the lifecycle of AI services including governance, observability, and experimentation.

What we're looking for

  • Bachelor's degree in Computer Science, AI, Electrical Engineering, or related fields with at least 4 years of experience developing AI/ML algorithms.
  • Master's degree in Computer Science, AI, Electrical Engineering, or related fields with at least 2 years of experience developing AI/ML algorithms.
  • At least 4 years of experience programming with Python, Go, Scala, or Java.
  • 6 years of experience deploying scalable and responsible AI solutions on cloud platforms like AWS, Google Cloud, or Azure.
  • Experience designing, developing, delivering, and supporting AI services including LLM inference, similarity search, and vector databases.
  • Experience applying state-of-the-art techniques to optimize training and inference software for hardware utilization, latency, throughput, and cost.
  • Ability to interpret scientific publications and apply novel research techniques in production environments.
  • Strong foundation in engineering and mathematics to identify and exploit optimization opportunities in hardware and software.

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