AI Engineer

Capital One

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

Posted 11/4/2026full-timeUnited StatesMid-LevelSenior💰 $229,900 - $286,200 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 optimizing AI systems, with a strong focus on foundation model training, LLM inference, 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 System OptimizationTechnical Leadership

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI Software DevelopmentFoundation Model TrainingLLM InferenceSimilarity SearchModel EvaluationMulti-Agent WorkflowsModel Optimization TechniquesDynamic Inference StrategiesModel CompressionEthical AI Standards

Soft Skills

MentoringCollaborationTechnical Vision ContributionGovernance Review Leadership

Tools & Technologies

AWS UltraclustersHugging FaceVectorDBsPyTorchGoogle CloudAzure

Industry Keywords

AI Engineering StandardsCost-Performance GovernanceScalable AI SolutionsOperational Cost ManagementHuman-In-The-Loop Review

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, VectorDBs, 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 to ensure technical consistency and compliance with AI engineering standards
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 one of these fields plus at least 4 years of experience
At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java
Experience leading development of AI systems with cost, latency, throughput, and accuracy tradeoff decisions
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 technologies such as 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
Experience architecting and integrating rule-based, retrieval-augmented, and generative components into unified production pipelines
Experience defining and enforcing ethical AI deployment standards, including explainability, fairness, and human-in-the-loop review
Ability to balance model performance and operational cost through dynamic inference strategies and model compression
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 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