AI Engineer 5 – AI Foundations, Evaluations, Agentic Workflows

Capital One

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

Posted 11/6/2026full-timeUnited StatesMid-LevelSenior💰 $229,900 - $286,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, developing, and optimizing AI systems, with a strong focus on foundation model training, multi-agent workflows, and cost-performance governance. Proven ability to lead technical teams and mentor engineers while ensuring compliance with AI engineering standards.

Highest-signal resume keywords

AI And ML Algorithm DevelopmentPython ProgrammingCloud Platform DeploymentAI Systems ArchitectureCost-Performance Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Software DevelopmentFoundation Model TrainingMulti-Agent WorkflowsModel Optimization TechniquesDynamic Inference StrategiesModel CompressionAgentic AI SystemsScalable AI SolutionsEthical AI Deployment StandardsGPU Utilization Tracking

Soft Skills

MentoringTechnical LeadershipCollaboration

Tools & Technologies

AWS UltraclustersHugging FaceVector DatabasesPyTorchGoogle CloudAzure

Certifications & Qualifications

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

Industry Keywords

AI Engineering StandardsModel EvaluationExperimentationGovernanceObservability

Tech Stack

Tools & technologies

AWSAzureCloudJavaPythonPyTorchScalaC++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 including AWS Ultraclusters, Hugging Face, vector databases, 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 elevate organizational technical maturity

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 Master's degree in those fields plus at least 4 years of such experience
At least 6 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
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 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
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
Ability to understand scientific publications and apply novel techniques in production
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 considered for a new qualified applicant
Reasonable accommodations for applicants who require them