AI Engineer 5, Gen AI Platform Services: Agentic AI

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Jose, CASan Francisco, CAMcLean, VACambridge, MANew York, NY
Salary
$229,900–$262,400 / yr
Employment
Full-time
Posted
4 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $207k
This role $246k
$156k $274k
below market most similar roles pay here above market

This role pays more than 84% of similar roles. Most pay $167,537–$246,150 — the blue band above. At the midpoint, this role pays about $246k versus about $207k 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 1891 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1020 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · AI Engineer 5, Gen AI Platform Services: Agentic AI

The AI Engineer 5 (Gen AI Platform Services: Agentic AI) joins the Intelligent Foundations and Experiences team to develop proprietary AI solutions for banking. You will partner with cross-functional teams to design, develop, test, deploy, and support AI software components. Key responsibilities include foundation model training, large language model inference, agents, multi-agent workflows, similarity search, guardrails, model evaluation, and observability. You will invent optimization techniques to improve scalability, cost, latency, and throughput for production systems. The role requires expertise in Python, Go, Scala, CUDA, or Java, alongside tools like AWS Ultraclusters, Huggingface, and VectorDBs. You will also manage cost-performance governance, lead design councils, and mentor other engineers. This role solves complex problems in building responsible, scalable, and high-performance AI infrastructure for enterprise-level banking services.

What does a AI Engineer earn in California?

Median $246150 from 82 postings across 11 companies.

See salary data

What you'll do

  • Design, develop, test, deploy, and support AI software components including LLM inference, agents, and multi-agent workflows.
  • Implement foundation model optimization techniques to improve scalability, cost, latency, and throughput of production systems.
  • Design and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models.
  • Lead cost-performance governance reviews to track GPU utilization, model throughput, and inference cost efficiency.
  • Lead design councils to ensure technical consistency and compliance with AI engineering standards.
  • Mentor Principal and Manager-level AI engineers to foster cross-domain learning and elevate organizational technical maturity.
  • Contribute to the technical vision and long-term roadmap of foundational AI systems.

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 and ML algorithms.
  • Master's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 4 years of experience developing AI and ML algorithms.
  • At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java.
  • Experience leading development of AI systems with tradeoff decisions around cost, latency, throughput, and accuracy (preferred).
  • 7+ years of experience deploying scalable and responsible AI solutions on cloud platforms (preferred).
  • Experience designing, developing, delivering, and supporting complex AI systems (preferred).
  • Experience developing AI and ML algorithms using Python, C++, C#, Java, CUDA, or Golang (preferred).
  • Experience developing and applying state-of-the-art techniques for optimizing training and inference software (preferred).

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