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AI Engineer 5 – MLXT
Capital One. Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and optimizing AI and ML algorithms, with a strong focus on cost, latency, and throughput in cloud environments. 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 Deployment (AWS, Google Cloud, Azure)AI System OptimizationTeam Leadership And Mentorship
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI Software DevelopmentFoundation Model TrainingLLM InferenceMulti-Agent WorkflowsModel EvaluationExperimentationGovernanceObservabilityCost-Performance GovernanceDynamic Inference Strategies
Soft Skills
MentoringCollaborationTechnical Vision Contribution
Tools & Technologies
AWS UltraclustersHugging FaceVector DatabasesPyTorchCUDAJavaGoScalaC++C#
Industry Keywords
Ethical AI Deployment StandardsExplainabilityFairnessHuman-In-The-Loop ReviewModel Compression
Tech Stack
Tools & technologiesAWSAzureCloudJavaPythonPyTorchScalaC++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, vector databases, 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 field 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 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 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
- 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 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
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