This role pays more than
52%
of similar roles. Most pay
$164,993–$246,150
— the shaded band above.
At the midpoint, this role pays about
$211k
versus about
$206k
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 (AI Foundations: LLM Customization, Finetuning, Reinforcement Learning) joins the Intelligent Foundations and Experiences team to develop and deploy proprietary AI solutions that enhance products for millions of customers. You will design, test, and support software components including foundation model training, large language model inference, multi-agent workflows, similarity search, guardrails, and model evaluation. The role involves optimizing performance metrics like latency, throughput, and cost while ensuring ethical alignment and governance. You will work with a stack featuring AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and programming languages including Python, Go, Scala, CUDA, and Java. This position addresses the technical challenge of building scalable, high-performance AI infrastructure to provide real-time, personalized experiences in the banking sector while managing complex systems at petabyte scale through advanced model optimization and inference pipelines.
What does a AI Engineer earn in New York?
Median $246150 from 66 postings across 9 companies.
Design, develop, test, and deploy AI software components including foundation model training and LLM inference.
Implement agentic workflows, similarity search, guardrails, and model evaluation systems for production use.
Develop state-of-the-art optimization techniques to improve performance, cost, latency, and throughput of large-scale AI systems.
Own the end-to-end architecture for complex AI systems while ensuring maintainability, observability, and ethical alignment.
Define and maintain service-level objectives (SLOs) 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 translating research into production.
What we're looking for
Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 4 years of experience developing AI/ML algorithms.
Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus 2 years of experience developing AI/ML algorithms.
At least 4 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).
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/ML algorithms like LLM inference, similarity search, and vector databases using various programming languages (preferred).
Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost (preferred).
Python
Go
CUDA
PyTorch
Huggingface
AWS
VectorDBs
LLM Inference
Distributed Systems
C++
Java
Scala
C#
Machine Learning
Multi-agent Workflows
Similarity Search
Model Evaluation
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
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