AI Engineer 5 – Gen AI Platform Services, Agentic Systems

Capital One

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

Posted 9/18/2026full-timeSan Francisco • California • United StatesMid-LevelSenior💰 $229,900 - $286,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing AI and ML algorithms, with a strong focus on large language models, multi-agent workflows, and cloud deployment. Proven ability to lead technical teams, establish governance standards, and mentor engineers to enhance organizational capabilities in AI systems.

Highest-signal resume keywords

AI And ML Algorithm DevelopmentPython ProgrammingAWS Cloud DeploymentLarge Language Model InferenceCost-Performance Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Software DevelopmentModel TrainingModel EvaluationMulti-Model OrchestrationAgentic AI SystemsModel CompressionDynamic Inference StrategiesSimilarity SearchVectorDBsFoundation Model Optimization

Soft Skills

MentoringLeadershipCollaborationTechnical Vision ContributionCommunication

Tools & Technologies

AWS UltraclustersHugging FacePyTorchGoogle CloudAzure

Industry Keywords

Ethical AI DeploymentExplainabilityFairnessHuman-In-The-Loop ReviewScalable AI Solutions

Tech Stack

Tools & technologies

AWSAzureCloudJavaOpen SourcePythonPyTorchScalaGo

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 foundation model optimization techniques to improve scalability, cost, latency, and throughput of large-scale 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 those 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 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 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 agentic workflows
Experience architecting and integrating rule-based, retrieval-augmented, and generative components 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
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position

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 who require them