AI Engineer 5 – AI Foundations, LLM Customization, Finetuning, Reinforcement Learning

Capital One

. Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products .

Posted 9/18/2026full-timeNew York City • California • United StatesMid-LevelSenior💰 $229,900 - $286,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing AI systems, including foundation model training, LLM inference, and multi-agent workflows. Proven ability to lead technical teams, enforce AI engineering standards, and implement cost-performance governance in cloud environments.

Highest-signal resume keywords

AI And ML Algorithm DevelopmentPython ProgrammingCloud Platform DeploymentAI System OptimizationTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Software DevelopmentFoundation Model TrainingLLM InferenceSimilarity SearchModel EvaluationMulti-Model OrchestrationAgentic AI SystemsDynamic Inference StrategiesModel CompressionEthical AI Standards

Soft Skills

Excellent CommunicationMentoringCross-Domain Learning

Tools & Technologies

AWS UltraclustersHugging FaceVectorDBsPyTorchGoogle CloudAzure

Industry Keywords

AI Engineering StandardsCost-Performance GovernanceExplainabilityFairnessHuman-In-The-Loop Review

Tech Stack

Tools & technologies

AWSAzureCloudJavaOpen SourcePythonPyTorchScalaC++Go

About the role

Key responsibilities & impact

Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products
Design, develop, test, deploy, and support AI software components including foundation model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability
Leverage Open Source and SaaS AI technologies such as AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch
Invent and introduce foundation model optimization techniques to improve scalability, cost, latency, and throughput of production AI systems
Contribute to the technical vision and long-term roadmap of foundational AI systems
Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models
Establish and lead cost-performance governance reviews, tracking GPU utilization, model throughput, and inference cost efficiency
Lead team design councils or design review boards for technical consistency and AI engineering standards compliance
Mentor Principal- and Manager-level AI engineers and foster cross-domain learning

Requirements

What you’ll need

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies; or a Master's degree in those fields plus at least 4 years of experience
At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java
Experience leading development of AI systems with cost, latency, throughput, and accuracy tradeoff decisions
7 years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud
Experience designing, developing, delivering, and supporting complex AI systems
Experience developing AI and ML algorithms or technologies including LLM inference, similarity search, VectorDBs, guardrails, and memory using Python, C++, C#, Java, CUDA, or Golang
Experience developing and applying state-of-the-art techniques for optimizing training and inference software
Experience building agentic AI systems and workflows
Experience architecting and integrating heterogeneous AI systems into unified production pipelines
Experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review processes
Demonstrated ability to balance model performance and operational cost through dynamic inference strategies and model compression
Experience right-sizing models, instance counts, and hardware types
Strong foundation in engineering and mathematics
Passion for staying abreast of AI research and applying novel techniques in production
Excellent communication and presentation skills
Capital One will consider sponsoring a new qualified applicant for employment authorization

Benefits

Comp & perks

Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive, and inclusive health, financial and other benefits supporting total well-being
Employment authorization sponsorship may be considered for a new qualified applicant
Reasonable accommodations for applicants who require them