Capital One

AI Engineer

• 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
• 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 AI reliability SLOs for latency, uptime, and model-performance drift
• Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate model-inference pipelines
• Lead technical reviews for AI deployments, ensuring security, data governance, and compliance standards
• Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation

AI Engineer 4, MLX, Agentic AI, Gen AI Platform Services

Developer Relations

• Lead, manage, and grow multiple teams of product-focused software engineers
• Define long-term technical strategy and architecture standards for enterprise platforms
• Partner with senior engineering and business leaders to align product roadmaps with scalable technology capabilities
• Drive talent strategy, succession planning, skill development, and equitable opportunity across engineering teams
• Mentor and guide engineers' professional and technical development
• Work with product managers to develop market-driven application capabilities and customer experiences
• Continuously improve software engineering practices
• Design, develop, test, implement, and support technical solutions across full-stack development tools and technologies
• Lead the craftsmanship, availability, resilience, and scalability of solutions
• Stay current with technology trends, experiment with new technologies, participate in technology communities, and mentor engineering community members
• Encourage innovation, cutting-edge technology adoption, inclusion, teamwork, self-organization, and diversity
• Improve engineering velocity across the organization
• Lead deployment of new and existing applications into AWS environments

Senior Director, Full-stack Engineer – Developer Experience

Supply Chain

• Lead product strategy, design, delivery, and consumer convergence for attraction, location, and non-bookable Inventory data
• Define clear and accessible domain entities
• Define links to bookable Inventory, Trip, and Customer entities for insight generation
• Identify frameworks for agentic consumption of Places data to support ML modeling
• Partner across COF and PPX teams to align Data Products with emerging standards
• Manage delivery of destination-aligned data assets in line with Travel use case needs
• Determine access patterns to Travel Data Platform assets and drive convergence
• Support data governance and well-managed agenda through existing or new capabilities and constructs
• Integrate third-party data into the Travel Data Platform and resolve data across sources to central data standards

Data Product Manager – Inventory, Places

Researcher

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products
• Use PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and other technologies to analyze numeric and textual data
• Build AI foundation models through design, training, evaluation, validation, and implementation
• Conduct applied research and advance AI developments into customer experiences
• Translate complex research into tangible business goals
• Lead cross-functional research threads connecting prototype model development and deployment with ML engineering partners
• Apply state-of-the-art AI research to banking products and customer experiences
• Pursue impactful research problems and long-running research projects

Applied Researcher 5

• 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 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 team design councils or design review boards for technical consistency and AI engineering standards compliance
• Mentor Principal- and Manager-level AI engineers and elevate organizational technical maturity

AI Engineer 5 – Gen AI Platform Services, Agentic AI

• 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
• 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

AI Engineer 5, AI Foundations, VLM Customization

• 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, 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 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 SLOs 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 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

• 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, agent 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 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

AI Engineer 5 – AI Foundations

Product Manager

• Reimagine the status quo and identify value creation opportunities
• Drive innovative and sustainable customer experiences through technology
• Obsess about internal and external customer needs to reimagine and innovate product solutions
• Deliver outcomes by focusing on leverage and execution excellence
• Leverage technology to deliver innovative and resilient solutions enabling near-term and long-term value
• Identify and resolve complex problems while mitigating product risks
• Lead cross-functional teams to solve customer problems and drive organizational alignment

Manager, Product Management – Auto

• 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 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 foster cross-domain learning

AI Engineer 5 – AI Foundations, LLM Core, Agentic AI

Compliance

• Analyze business initiatives, including new products and processes, to determine compliance with applicable privacy laws and regulations
• Assess and balance risk when providing compliance advice to the business
• Maintain expert knowledge of applicable laws and regulations and advise the business on current developments
• Interact with business partners to influence processes, procedures, and controls
• Assess impacts and advise on remediation of breakdowns or findings, including consumer complaints
• Engage with subject matter experts and transaction testing team members
• Identify trends in compliance data and proactively advise on internal and external risks
• Ensure the team fulfills expectations and deliverables under the compliance management program
• Lead the Privacy team supporting Capital One Auto and oversee compliance with applicable federal and state privacy laws and regulations
• Deliver quality results and provide compliance and privacy risk advice as a subject matter expert

Director, Privacy Compliance

Insurance

• Lead within the Risk Management Framework and Control Methodology team
• Architect and operationalize the project program roadmap
• Implement appropriate governance and drive progress across a complex, matrixed environment
• Lead a specialized reporting, insights, and GRC team
• Transform data into forward-leaning outcomes that influence decision-making
• Ensure business requirements are managed and implemented within reporting tools and GRC
• Empower and lead a high-performing team of risk professionals
• Manage complex, cross-functional projects, processes, and program integrations
• Build and maintain a detailed roadmap and develop project approaches
• Establish governance and tools such as project plans, RAID, and go-live requirements
• Champion project documentation and design standards to mitigate delivery risk
• Advance reporting and monitoring with tools providing real-time insights into risk framework and control methodology adherence
• Enhance GRC systems and integrate AI and automation into control management and testing protocols
• Build collaborative relationships and partner cross-functionally to drive delivery

Director, Risk Management

Artificial Intelligence

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products
• Leverage PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and other technologies to analyze numeric and textual data
• Build AI foundation models through design, training, evaluation, validation, and implementation
• Conduct applied research to advance AI developments into customer experiences
• Translate complex technical work into tangible business goals
• Lead cross-functional research threads bridging prototype model development and deployment with ML engineering partners
• Ensure scientific insights translate into production impact
• Research and evaluate emerging technologies and state-of-the-art methods
• Identify and improve impactful machine learning problems and solutions
• Lead research initiatives and guide junior researchers or engineers through experimental design and evaluation

Applied Researcher 5, AI Foundations, LLM Core, Agentic AI

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products
• Leverage PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and other technologies to analyze numeric and textual data
• Build AI foundation models through design, training, evaluation, validation, and implementation
• Conduct applied research and advance emerging AI developments into customer experiences
• Translate complex research into tangible business goals
• Collaborate with senior researchers on internal technical reports or conference submissions
• Research and evaluate emerging technologies and state-of-the-art methods
• Identify impactful research problems and pursue an autonomous research agenda
• Develop talent and challenge conventional thinking with stakeholders

Applied Researcher 4 – AI Foundations, LLM Core, Agentic AI

• 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
• 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 SLOs 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
• Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation

AI Engineer 4, MLX, Agentic AI, Gen AI Platform Services

Senior Director, Full-stack Engineer – Developer Experience

Data Product Manager – Inventory, Places

Applied Researcher 5

AI Engineer 5 – Gen AI Platform Services, Agentic AI

AI Engineer 5, AI Foundations, VLM Customization

AI Engineer 4, MLX, Agentic AI, Gen AI Platform Services

AI Engineer 5 – AI Foundations

Manager, Product Management – Auto

AI Engineer 5 – AI Foundations, LLM Core, Agentic AI

Director, Privacy Compliance

Director, Risk Management

Applied Researcher 5, AI Foundations, LLM Core, Agentic AI

Applied Researcher 4 – AI Foundations, LLM Core, Agentic AI

AI Engineer 4, AI Foundations, VLM Customization