AI Engineer – Level 4

Capital One

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

Posted 9/29/2026full-timeUnited 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 and ML algorithms, with a strong focus on optimizing performance and scalability. Proficient in leading cross-functional teams to ensure ethical alignment and compliance in AI systems.

Highest-signal resume keywords

AI And ML Algorithm DevelopmentPython ProgrammingCloud Platform DeploymentAI Model GovernanceDistributed Systems Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Software DevelopmentFoundation Model TrainingLarge Language Model InferenceSimilarity SearchModel EvaluationPerformance TuningScalable AI SolutionsAgentic AI SystemsTraining OptimizationProducibility Processes

Soft Skills

MentoringCollaborationInfluencing Architectural Decisions

Tools & Technologies

AWS UltraclustersHugging FaceVectorDBsPyTorchGPU UtilizationTPU UtilizationCloud Platforms

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's Degree in AI or Related Fields

Industry Keywords

AI SystemsMachine LearningData GovernanceEthical AICompliance Standards

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, large language model 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 service-level objectives 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 a Master's degree in those fields plus at least 2 years of experience developing AI and ML algorithms or technologies
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, including LLM inference, similarity search, VectorDBs, guardrails, and memory
Experience with 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
Ability to work in the United States; 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
Reasonable accommodations for applicants with disabilities