This role pays more than
62%
of similar roles. Most pay
$165,875–$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
998 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 992 roles with salary data.
Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) joins the Intelligent Foundations and Experiences team to develop proprietary solutions that power core business functions. This role involves collaborating with cross-functional teams of engineers, scientists, and product managers to design, test, deploy, and support critical AI software components. Key responsibilities include foundation model training, large language model inference, similarity search, guardrails, model evaluation, and observability. The candidate will implement state-of-the-art LLM optimization techniques to improve performance metrics like scalability, cost, latency, and throughput for production systems. Required technical skills include proficiency in Python, Go, Scala, or Java, along with experience using tools such as AWS Ultraclusters, Huggingface, VectorDBs, and Nemo Guardrails. The role focuses on building scalable, high-performance AI infrastructure to solve complex problems within the banking sector through advanced machine learning engineering.
Design, develop, test, deploy, and support AI software components including foundation model training and LLM inference.
Implement similarity search, guardrails, model evaluation, and observability for production AI systems.
Develop state-of-the-art LLM optimization techniques to improve performance, scalability, cost, latency, and throughput.
Utilize a broad stack of open-source and SaaS technologies including Hugging Face, VectorDBs, and PyTorch.
Contribute to the technical vision and long-term roadmap of foundational AI systems.
Translate scientific research into practical production techniques for large-scale AI applications.
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, or Java.
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.