AI Engineer 5, FM Hosting, LLM Inference

Capital One

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

Posted 9/24/2026full-timeUnited 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 in AI deployments.

Highest-signal resume keywords

AI Systems DevelopmentPython ProgrammingCloud Deployment (AWS, Google Cloud, Azure)AI/ML Algorithm DevelopmentCost-Performance Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Software DevelopmentMachine Learning AlgorithmsFoundation Model TrainingLLM InferenceSimilarity SearchModel OptimizationMulti-Model OrchestrationDynamic Inference StrategiesModel CompressionEthical AI Standards

Soft Skills

MentoringCross-Domain LearningTechnical Leadership

Tools & Technologies

AWS UltraclustersHugging FaceVectorDBsPyTorchCUDAJavaGoScalaC++C#

Industry Keywords

AI Engineering StandardsCost EfficiencyLatency OptimizationThroughput ManagementModel Evaluation

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 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 experience
At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java
Experience leading AI systems development with cost, latency, throughput, and accuracy tradeoffs
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/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
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 based on requirements
Strong engineering and mathematics foundation
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 accommodations for applicants who require them