AI Engineer 4 – Vision Model Customization

Capital One

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

Posted 9/24/2026full-timeUnited StatesJuniorMid-Level💰 $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 foundation model optimization, scalability, and ethical governance. Proficient in leveraging cloud platforms and open-source technologies to deliver reliable AI solutions.

Highest-signal resume keywords

AI System DevelopmentPython ProgrammingCloud Deployment (AWS, Google Cloud, Azure)Foundation Model OptimizationDistributed Systems Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI AlgorithmsMachine Learning TechnologiesModel TrainingModel InferencePerformance TuningScalable AI SolutionsGovernance ProcessesSimilarity SearchMulti-Agent WorkflowsEvaluation Techniques

Soft Skills

MentoringCollaborationTechnical Leadership

Tools & Technologies

AWS UltraclustersHugging FacePyTorchVector DatabasesGPU/TPU Utilization

Certifications & Qualifications

Bachelor's Degree in Computer Science, AI, Electrical Engineering, or Related FieldsMaster's Degree in Computer Science, AI, Electrical Engineering, or Related Fields

Industry Keywords

AI GovernanceData GovernanceEthical AIModel Performance DriftProducibilityLineage Tracking

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, multi-agent workflows, similarity search, guardrails, evaluation, experimentation, governance, and observability
Leverage open-source and SaaS AI technologies such as AWS Ultraclusters, Hugging Face, vector databases, 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 while 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 model inference pipelines
Lead technical reviews for AI system deployments, ensuring security, data governance, and compliance
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 one of these 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 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

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
Reasonable accommodations for applicants who require them
Equal opportunity and non-discrimination protections
Drug-free workplace