Staff AI Engineer – Enterprise Analysis Platform

Capital One

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

Posted 9/29/2026full-timeRemote • United StatesLead💰 $244,700 - $335,100 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing, developing, and deploying AI systems with a focus on scalability, cost, and performance optimization. Proven ability to lead engineering teams and influence cross-functional stakeholders while championing responsible AI principles.

Highest-signal resume keywords

AI System DesignPython ProgrammingAWS DeploymentLLM InferenceTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI AlgorithmsMachine Learning TechnologiesFoundation Model OptimizationModel RoutingSimilarity SearchCUDA ProgrammingMulti-Agent WorkflowsData Pipeline GovernanceModel HandoffNorth-Star Metrics Definition

Soft Skills

Excellent CommunicationMentorshipInfluencing Stakeholders

Tools & Technologies

AWS UltraclustersHugging FaceVectorDBsPyTorchGoogle CloudAzure

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's Degree in Related Field

Industry Keywords

Responsible AI PrinciplesEnterprise AI ArchitectureCloud PlatformsFederated AI StrategiesAI Governance

Tech Stack

Tools & technologies

AWSAzureCloudJavaPythonPyTorchScalaGo

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 including 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
Set technical direction for enterprise-wide AI architecture, tooling, observability, and deployment standards
Own design and integration of model routing, caching, and orchestration systems for hybrid and multi-model workloads
Champion responsible AI principles, including transparency, reproducibility, and fairness-by-design
Drive internal education, mentorship, and best-practice dissemination through architecture councils and AI guilds

Requirements

What you’ll need

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies; or Master's degree in a related field plus at least 6 years of such experience
At least 8 years of programming experience with Python, Go, Scala, CUDA, or Java
Experience designing AI systems with cost, latency, throughput, and accuracy tradeoffs
8+ years deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud
Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems
Ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to VP level
Experience developing AI/ML technologies including LLM inference, similarity search, VectorDBs, guardrails, and memory
Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
Experience building agentic AI systems and workflows
Excellent communication and presentation skills for articulating complex AI concepts
Track record defining and operationalizing enterprise AI architecture standards, data pipeline governance, observability, and evaluation frameworks
Experience leading federated or multi-cloud AI strategies
Success influencing research-to-production promotion, model handoff, evaluation, and productization
Experience defining north-star metrics for AI systems
Experience right-sizing models, instance counts, and hardware types
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
Employment authorization sponsorship may be considered for a new qualified applicant
Reasonable accommodations for applicants who require them