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Capital One

AI Engineer 4, AI Foundations, LLM Customization, Finetuning

Capital One

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

Posted 9/22/2026full-timeUnited StatesJuniorMid-Level💰 $215,200 - $245,600 per yearWebsite

Core Competencies

Role fit
Core Competencies

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Demonstrates expertise in developing and deploying AI and ML algorithms, with a strong focus on optimizing performance, scalability, and ethical governance. Proficient in leading technical architecture and ensuring compliance across AI systems while mentoring team members in best practices.

Highest-signal resume keywords
AI And ML Algorithm DevelopmentPython ProgrammingCloud Platform DeploymentDistributed Systems DesignAI Model Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
AI Software DevelopmentFoundation Model TrainingLLM InferenceModel EvaluationPerformance TuningScalable AI SolutionsAgentic AI SystemsTraining OptimizationInference OptimizationData Governance
Soft Skills
MentoringCollaborationTechnical Leadership
Tools & Technologies
AWS UltraclustersHugging FaceVectorDBsPyTorchGPU UtilizationTPU Utilization
Industry Keywords
AI EthicsModel Performance DriftSLO DefinitionProducibilityLineage Tracking

Tech Stack

Tools & technologies
AWSCloudDistributed SystemsJavaOpen 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, 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
  • 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 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 a Master's degree in those fields plus at least 2 years of such experience
  • At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java
  • Experience leading AI system development with cost, latency, throughput, and accuracy tradeoffs
  • 6 years of experience deploying scalable and responsible AI solutions on cloud platforms
  • 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 optimizing training and inference software for hardware utilization, latency, throughput, and cost
  • Experience building agentic AI systems and 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

Benefits

Comp & perks
  • Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI)
  • Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being
  • Employment authorization sponsorship considered for a new qualified applicant