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AI Engineer 5, MLX, Agentic AI, Gen AI Platform Services
Capital OneCore Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing, developing, and optimizing AI systems, with a strong focus on foundation model training, LLM inference, and multi-model orchestration. Proven ability to lead technical governance, mentor engineering teams, and implement cost-performance strategies in AI deployments.
Highest-signal resume keywords
AI Software DevelopmentPython ProgrammingLLM InferenceCost-Performance GovernanceMulti-Model Orchestration
Tech Stack
Tools & technologiesAbout 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 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 those fields plus at least 4 years of experience
- At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java
- Preferred: Experience leading development of AI systems with cost, latency, throughput, and accuracy tradeoff decisions
- Preferred: 7+ years of experience deploying scalable and responsible AI solutions on cloud platforms
- Preferred: Experience designing, developing, delivering, and supporting complex AI systems
- Preferred: Experience with LLM inference, similarity search, VectorDBs, guardrails, and memory
- Preferred: Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
- Preferred: Experience building agentic AI systems and workflows
- Preferred: Experience architecting heterogeneous AI systems and unified production pipelines
- Preferred: Experience defining and enforcing ethical AI deployment standards
- Preferred: Ability to balance model performance and operational cost through dynamic inference strategies and model compression
- Preferred: Experience right-sizing models, instance counts, and hardware types
- Passion for AI research and applying novel techniques in production
- Strong foundation in engineering and mathematics
- 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 consideration for a new qualified applicant
- Reasonable accommodations for applicants who require them
- Drug-free workplace
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 AlgorithmsMachine Learning TechnologiesFoundation Model OptimizationCUDA ProgrammingJava ProgrammingGo ProgrammingScala ProgrammingModel EvaluationExperimentationDynamic Inference Strategies
Soft Skills
MentoringCross-Domain LearningTechnical Vision Contribution
Tools & Technologies
AWS UltraclustersHugging FaceVectorDBsPyTorchAI Engineering Standards
Industry Keywords
AI SystemsEthical AI DeploymentScalable AI SolutionsGPU UtilizationOperational Cost Management