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AI Engineer 4, AI Foundations, VLM Customization
Capital OneCore Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and deploying AI systems, with a strong focus on foundation model optimization, AI governance, and scalable architecture. Proficient in leveraging cloud platforms and open-source technologies to enhance AI performance and reliability.
Highest-signal resume keywords
AI System DevelopmentFoundation Model OptimizationCloud Platform DeploymentAI Governance ProcessesDistributed Systems Design
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 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
- Contribute to the technical vision and long-term roadmap of foundational AI systems
- Own end-to-end architecture for complex AI systems, ensuring maintainability, observability, and ethical alignment
- Define and maintain service-level objectives for AI reliability, including latency, uptime, and model performance drift
- Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate model inference pipelines
- Lead cross-functional technical reviews for AI system deployments, ensuring security, data governance, and compliance standards
- Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation
Requirements
What you’ll need- Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies, OR a Master's degree in those fields plus at least 2 years of such experience
- At least 4 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
- 6 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 AI services
- 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
- Proficiency designing distributed systems for model training, evaluation, and online inference at petabyte scale
- Experience defining AI model governance processes, including producibility, lineage tracking, and automated retraining schedules
- Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms
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
- Reasonable accommodations for applicants who require them
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonGoScalaCUDAJavaAI/ML AlgorithmsModel TrainingModel InferencePerformance TuningScalable AI Solutions
Soft Skills
MentoringCollaborationInfluencing Architectural Decisions
Tools & Technologies
AWS UltraclustersHugging FaceVectorDBsPyTorchGPU/TPU OptimizationCloud Platforms
Industry Keywords
AI GovernanceModel EvaluationData GovernanceEthical AlignmentService-Level Objectives