This role pays more than
62%
of similar roles. Most pay
$161,500–$237,187
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$199k
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
878 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 872 roles with salary data.
AI Engineer 4 (Gen AI Platform Services: Agentic AI, Agent Guardrails, Agent Evaluation, Agent Memory) joins the Intelligent Foundations and Experiences team to develop responsible, scalable AI systems for banking applications. This role involves designing, developing, testing, and supporting software components including foundation model training, large language model inference, multi-agent workflows, similarity search, guardrails, and model evaluation. The engineer will optimize performance metrics like latency, throughput, and cost while managing end-to-end architecture, governance, and observability for complex systems. The technical stack includes Python, Go, Scala, CUDA, Java, C++, C#, PyTorch, Huggingface, VectorDBs, and AWS Ultraclusters. Key responsibilities include mentoring associates, collaborating with infrastructure teams to optimize GPU/TPU utilization, and translating research into production-ready solutions. The role addresses the challenge of building high-performance AI infrastructure to provide personalized customer experiences and internal tools.
What does a AI Engineer earn in California?
Median $246150 from 73 postings across 11 companies.
Develop and support AI software components including LLM inference, agentic workflows, and similarity search.
Implement state-of-the-art optimization techniques to improve performance, scalability, cost, and latency of production systems.
Design end-to-end architectures for complex AI systems ensuring maintainability, observability, and ethical alignment.
Define and maintain service-level objectives for AI reliability including uptime and model performance drift.
Collaborate with infrastructure teams to optimize GPU/TPU utilization and accelerate inference pipelines.
Lead technical reviews to ensure new AI deployments meet security, data governance, and compliance standards.
Mentor senior associates on scalable design, performance tuning, and research-to-production translation.
What we're looking for
Bachelor'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.
Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 2 years of experience developing AI and ML algorithms or technologies.
At least 4 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).
6 years of experience deploying scalable and responsible AI solutions on cloud platforms (preferred).
Experience designing, developing, delivering, and supporting AI services (preferred).
Experience developing AI and ML algorithms or technologies such as LLM Inference, Similarity Search, VectorDBs, Guardrails, and Memory using Python, C++, C#, Java, CUDA, or Golang (preferred).
Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost (preferred).
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Agentic AI
Multi-agent Workflows
Similarity Search
Model Evaluation
Model Optimization
C++
Scala
Java
C#
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
C++
Java
Scala
C#
SaaS
Machine Learning
Gen AI
Model Evaluation
Observability
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
C++
Java
Scala
C#
SaaS
Machine Learning
Gen AI
Model Evaluation
Observability
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Multi-agent Workflows
Similarity Search
Model Evaluation
Observability
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Model Evaluation
Similarity Search
SaaS
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Agentic AI
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Model Optimization
Similarity Search
SaaS