AI Engineer 5 – Gen AI Platform Services, Agentic AI, Guardrails, Evaluation

Capital One

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

Posted 9/18/2026full-timeNew York City • 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 in AI deployments.

Highest-signal resume keywords

AI And ML Algorithm DevelopmentPython ProgrammingCloud Platform DeploymentAI System OptimizationEthical AI Standards Compliance

ATS Keywords

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Hard Skills

Foundation Model TrainingLLM InferenceSimilarity SearchModel EvaluationMulti-Agent WorkflowsAI Software DevelopmentModel Optimization TechniquesDynamic Inference StrategiesAgentic AI SystemsModel Compression

Soft Skills

Excellent CommunicationPresentation SkillsMentoring

Tools & Technologies

AWS UltraclustersHugging FaceVectorDBsPyTorchAWSGoogle CloudAzure

Industry Keywords

AI Engineering StandardsCost-Performance GovernanceEthical AI DeploymentHuman-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 state-of-the-art 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 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 a Master's degree in those 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 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 using 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 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
Qualified applicants must meet applicable employment authorization requirements; Capital One will consider sponsoring a new qualified applicant

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
Employment authorization sponsorship may be considered for a new qualified applicant