AI Engineer 5 – AI Foundations, LLM Core, Agentic AI

Capital One

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

Posted 9/18/2026full-timeSan Jose • 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 systems, including foundation model training and LLM inference, while ensuring compliance with ethical AI standards. Proven ability to lead technical teams and manage cost-performance governance for scalable AI solutions.

Highest-signal resume keywords

AI And ML Algorithm DevelopmentPython ProgrammingAWS Cloud DeploymentAI System OptimizationTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Software DevelopmentFoundation Model TrainingLLM InferenceSimilarity SearchModel EvaluationMulti-Agent WorkflowsCost-Performance OptimizationDynamic Inference StrategiesModel CompressionAgentic AI Systems

Soft Skills

Excellent CommunicationPresentation SkillsMentoring

Tools & Technologies

AWS UltraclustersHugging FaceVectorDBsPyTorchGoogle CloudAzure

Industry Keywords

Ethical AI DeploymentExplainabilityFairnessHuman-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 such as 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 foster cross-domain learning

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 a Master's degree in these 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 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 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
Excellent communication and presentation skills
Experience architecting and integrating rule-based, retrieval-augmented, and generative AI components into unified production pipelines
Experience defining and enforcing standards for ethical AI deployment, 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 based on requirements
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
Reasonable accommodation support
Equal opportunity and non-discrimination protections
Drug-free workplace