This role pays more than
69%
of similar roles. Most pay
$188,675–$254,750
— the shaded band above.
At the midpoint, this role pays about
$246k
versus about
$222k
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.
As an AI Engineer 5 (FM Hosting, LLM Inference) on the Intelligent Foundations and Experiences team, you will develop and support critical AI software components including foundation model training, large language model inference, multi-agent workflows, similarity search, and guardrails. You will collaborate with cross-functional teams to design and optimize production systems for performance, scalability, cost, and latency. The role involves building multi-model orchestration pipelines that integrate LLMs and vector search into unified systems while leading governance reviews on GPU utilization and inference efficiency. You will utilize a technical stack including PyTorch, Huggingface, VectorDBs, and AWS Ultraclusters. Required skills include proficiency in Python, Go, Scala, CUDA, or Java to solve complex problems in the domain of high-performance AI infrastructure and large-scale production systems for banking services.
What does a AI Engineer earn in California?
Median $246150 from 73 postings across 11 companies.
Develop and support AI components including foundation model training, LLM inference, agentic workflows, and similarity search.
Implement state-of-the-art optimization techniques to improve performance, scalability, cost, latency, and throughput of production systems.
Design and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models into unified systems.
Lead cost-performance governance reviews by tracking GPU utilization, model throughput, and inference cost efficiency.
Lead design councils to ensure technical consistency and compliance with established AI engineering standards.
Mentor senior engineers to foster cross-domain learning and improve organizational technical maturity.
Translate complex research papers into practical production techniques for large-scale 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/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, CUDA, or Java.
Experience leading development of AI systems with trade-offs regarding 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/ML algorithms using Python, C++, C#, Java, CUDA, or Golang (preferred).
Experience optimizing training and inference software to improve hardware utilization, latency, throughput, and cost (preferred).
LLM
Python
PyTorch
CUDA
Huggingface
VectorDBs
AWS
Go
Scala
C++
Java
C#
Agentic AI
Model Optimization
Multi-agent Workflows
Similarity Search
Machine Learning
LLM
Python
PyTorch
CUDA
Huggingface
VectorDBs
AWS
Go
Scala
C++
Java
C#
Agentic AI
Model Optimization
Multi-agent Workflows
Similarity Search
Machine Learning
LLM
Python
PyTorch
CUDA
Huggingface
VectorDBs
AWS
Go
Scala
C++
Java
C#
Agentic AI
Model Optimization
Multi-agent Workflows
Similarity Search
Machine Learning
LLM
PyTorch
Huggingface
Python
CUDA
Go
Scala
C++
Java
AWS
VectorDBs
Distributed Systems
Machine Learning
Reinforcement Learning
Multi-agent Workflows
Similarity Search
Model Inference