FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.

AI Engineer 5 – Gen AI Platform Services, Agentic AI, Guardrails, Evaluations
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 systems, including LLM inference and multi-agent workflows, while ensuring compliance with ethical AI standards. Proven ability to lead technical teams and mentor engineers in delivering scalable AI solutions on cloud platforms.
Highest-signal resume keywords
AI System DevelopmentPython ProgrammingLLM InferenceCloud DeploymentEthical AI Standards
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 TechnologiesModel OptimizationMulti-Model OrchestrationGPU UtilizationCost-Performance GovernanceDynamic Inference StrategiesModel CompressionSimilarity SearchAgentic AI Systems
Soft Skills
Excellent CommunicationPresentation SkillsMentoring
Tools & Technologies
AWS UltraclustersHugging FaceVectorDBsPyTorchCUDA
Industry Keywords
AI Engineering StandardsScalabilityLatencyThroughputExplainabilityFairnessHuman-in-the-Loop Review
Tech Stack
Tools & technologiesAWSCloudJavaOpen SourcePythonPyTorchScalaGo
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
- Work on 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
- 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 field plus at least 6 years of experience developing AI and ML algorithms or technologies; or a Master's degree in one of these fields plus at least 4 years of such experience
- At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java
- Experience leading AI system development with cost, latency, throughput, and accuracy tradeoffs
- 7+ years of experience deploying scalable and responsible AI solutions on cloud platforms
- Experience designing, developing, delivering, and supporting complex AI systems
- Experience developing AI/ML technologies including LLM inference, similarity search, VectorDBs, guardrails, and memory
- Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
- Experience building agentic AI systems and workflows
- Experience architecting and integrating heterogeneous AI systems 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
- Strong foundation in engineering, mathematics, hardware, software, and AI
- Ability to understand scientific publications and apply novel techniques in production
- Excellent communication and presentation skills
Benefits
Comp & perks- Performance-based incentive compensation, including cash bonuses and/or long-term incentives (LTI)
- Comprehensive 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