AI Engineer 4 – 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💰 $197,300 - $245,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying AI systems, with a strong focus on optimizing performance, scalability, and ethical governance. Proficient in leveraging cloud platforms and open-source technologies to deliver innovative AI solutions.

Highest-signal resume keywords

AI System DevelopmentPython ProgrammingCloud Platform DeploymentModel Governance ProcessesDistributed Systems Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI AlgorithmsMachine Learning TechnologiesFoundation Model OptimizationModel TrainingModel InferencePerformance TuningAgentic AI SystemsSLO DefinitionData GovernanceScalable AI Solutions

Soft Skills

MentoringCollaborationInfluencing Architectural Decisions

Tools & Technologies

AWSGoogle CloudAzureHugging FaceVectorDBsPyTorchCUDAGPU OptimizationTPU UtilizationSaaS AI Technologies

Industry Keywords

AI-Powered ProductsEthical AlignmentObservabilityLatencyUptimeModel Performance DriftProducibilityLineage TrackingAutomated RetrainingCross-Functional Technical Reviews

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsJavaPythonPyTorchScalaC++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 state-of-the-art foundation model optimization techniques to improve scalability, cost, latency, and throughput
Contribute to the technical vision and long-term roadmap of foundational AI systems
Own end-to-end architecture for complex AI systems, ensuring maintainability, observability, and ethical alignment
Define and maintain SLOs for AI reliability, including latency, uptime, and model performance drift
Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate model inference pipelines
Lead cross-functional technical reviews for AI system deployments, ensuring security, data governance, and compliance standards
Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation

Requirements

What you’ll need

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; or Master's degree in those fields plus at least 2 years of experience
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
6 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 AI services
Experience developing AI and ML algorithms or technologies 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 agentic workflows
Proficiency designing distributed systems for model training, evaluation, and online inference at petabyte scale
Experience defining AI model governance processes, including producibility, lineage tracking, and automated retraining schedules
Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position

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
Reasonable accommodations for applicants who require them