Capital One

Revenue Operations

• Operate as a Strategic Consultant and Customer-Centric Partner to auto dealers within the Capital One Sales Process
• Prospect and secure net-new subscribers for the partnering sales division
• Drive market share and revenue growth across assigned territories
• Sell the full suite of Navigator Platform products, bundles, and marketing solutions
• Partner with Regional leaders to identify high-opportunity customers and maximize time and ROI
• Demonstrate product knowledge and deliver compelling product demos
• Uncover customer friction points and convert them into growth opportunities
• Maintain a strong commitment to driving customer success
• Build rapport with auto dealer decision makers, influence decisions, and drive the company’s bottom line

Dealer Success Manager – Revenue

Full-stack Engineer

• Define the future of banking in the cloud as a Staff Engineer at Capital One
• Provide architectural oversight and technical guidance for Settlement engineering teams rebuilding the platform in the cloud
• Contribute hands-on proof-of-concept work
• Articulate and evangelize a technical vision
• Decompose complex problems into practical, operational solutions
• Ensure quality of technical design and implementation
• Advise on performance, scalability, operability, quality, security, reliability, and DevOps
• Handle multiple projects and maximize organizational impact
• Mentor engineers and strengthen technical expertise across engineering and product communities
• Modernize monolithic systems into microservice architectures and event-driven platforms
• Collaborate with senior engineering and architecture leaders on platform strategy and API standards
• Lead cross-divisional projects and architectural reviews
• Create communications, code samples, blog posts, and other knowledge-sharing materials
• Recruit and develop next-generation technical talent

Staff Full-stack Engineer

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 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 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 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

AI Engineer 4 – AI Foundations

• Lead a portfolio of diverse technology projects and a team of developers with experience in distributed microservices and full-stack systems
• Create solutions that help meet the company's regulatory needs
• Stay current with technology trends, experiment with and learn new technologies, and participate in internal and external technology communities
• Mentor members of the engineering community
• Collaborate with digital product managers to deliver robust cloud-based solutions
• Utilize Java, Scala, Python, Go, Node.js, JavaScript, TypeScript, open-source RDBMS and NoSQL databases, Docker, Kubernetes, and AWS tools and services
• Provide technical leadership across multiple teams and shape system architecture and long-term technology direction
• Drive end-to-end design and implementation of large-scale, distributed, or cloud-native systems with high reliability and performance requirements
• Partner with architects and product leaders to define platform roadmaps and shared-services strategy

Full-Stack Engineer, Level 5

• Lead a portfolio of diverse technology projects and a team of developers with distributed microservices and full-stack systems experience
• Create solutions that help meet regulatory needs
• Stay current with technology trends and experiment with emerging technologies
• Participate in internal and external technology communities
• Mentor engineering community members
• Occasionally code or evaluate code
• Collaborate with digital product managers to deliver robust cloud-based solutions
• Lead teams using Java, Scala, Python, Go, Node.js, JavaScript, TypeScript, open-source RDBMS and NoSQL databases, Docker, Kubernetes, and AWS tools and services
• Lead multiple Agile engineering teams or programs
• Provide strategic direction and ensure delivery of large-scale, cross-functional initiatives
• Partner with product, architecture, and platform leaders on multi-year technology roadmaps and investment priorities
• Develop and manage Lead Engineers and leads
• Build succession plans and scale leadership capability
• Align engineering execution with enterprise goals and influence portfolio-level decisions
• Drive end-to-end design and implementation of large-scale, distributed, or cloud-native systems

Senior Full-stack Engineer

• Lead, manage, and grow multiple teams of product-focused software engineers
• Mentor and guide the professional and technical development of engineers
• Work with product managers to understand desired application capabilities and testing scenarios
• 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 and learn new technologies, participate in internal and external technology communities, and mentor engineering community members
• Encourage innovation, cutting-edge technology adoption, inclusion, outside-the-box thinking, teamwork, self-organization, and diversity
• Improve the velocity of the team and other teams
• Lead deployment of new and existing applications into AWS environments
• Break monolithic applications into microservice architectures and build new microservices
• Develop customer-facing web applications and services for high-visibility features and products

Director, Full-Stack Engineer, Shopping Tech

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 and advance AI developments into next-generation customer experiences
• Translate complex research into tangible business goals
• Collaborate with senior researchers to prepare internal technical reports or conference submissions on novel methods or findings

Applied Researcher 4 – AI Foundations, LLM, Optimization, Finetuning

Risk

• Contribute to the development and execution of a sustainable enterprise policy program
• Develop and deliver communications and presentations through ERM communication channels and CPO-led forums
• Write, edit, and deliver communications supporting risk management functions, practices, and activities
• Review policy documents and partner with stakeholders to drive consistency and improve content
• Conduct effective challenge on policy documents throughout the policy management lifecycle
• Monitor adherence to the enterprise policy program and evaluate adherence practices
• Consult and support stakeholders in establishing required policy adherence practices
• Analyze policy management data to identify risks, process improvements, and training needs
• Lead specialized initiatives to evolve and improve the company’s policy program
• Develop training, tools, and resources for managing policy documents
• Maintain direct relationships with policy stakeholders and advise on policy creation and management
• Identify issues for escalation to appropriate CPO forums
• Collaborate with senior leadership across Operational Risk Management, Compliance, Legal, Technology, Internal Audit, and Regulatory Relations

Risk Manager

• Lead a portfolio of diverse technology projects and a team of developers with experience in distributed microservices and full-stack systems
• Create solutions that help meet regulatory needs for Capital One
• Stay current with technology trends and experiment with emerging technologies
• Participate in internal and external technology communities
• Mentor members of the engineering community
• Collaborate with digital product managers to deliver robust cloud-based solutions
• Use Java, Scala, Python, Go, Node.js, JavaScript, TypeScript, open-source RDBMS and NoSQL databases, Docker, Kubernetes, and AWS tools and services
• Provide technical leadership across multiple teams
• Shape system architecture and long-term technology direction
• Drive end-to-end design and implementation of large-scale, distributed, or cloud-native systems
• Partner with architects and product leaders to define platform roadmaps and shared-services strategy

• 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
• 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 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

AI Engineer 4 – AI Foundations, LLM Core, Agentic AI

• Lead, manage, and grow multiple teams of product-focused software engineers
• Shape 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 the professional and technical development of engineers
• Work with product managers to define application capabilities, testing scenarios, and customer experiences
• Continuously improve software engineering practices
• Design, develop, test, implement, and support technical solutions across full-stack technologies within and across Agile teams
• 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, Shopping Tech

• Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products
• Oversee design, development, testing, deployment, and support of AI software components
• Make build-vs-buy decisions across open-source and SaaS AI technologies
• Develop foundation model optimization techniques to improve scalability, cost, latency, and throughput
• Contribute to the technical vision and long-term roadmap of foundational AI systems
• Attract and retain AI talent and support team development through mentoring and coaching
• Translate enterprise AI goals into team roadmaps with measurable outcomes and regular readouts
• Operationalize Responsible AI through review gates, documentation and evaluation requirements, and rollout/rollback standards
• Create collaboration interfaces with Research, Data, and Platform teams to accelerate model lifecycle

Senior Manager, AI Engineer – IFX

Project Manager

• Drive strategy and execution of large-scale, complex, cross-functional projects aligned with Capital One’s business priorities
• Break down complexity and organizational silos
• Influence roadmaps and dive deeply into project plans and scope
• Make strategic tradeoffs and drive platform consolidation
• Identify, resolve, define, and escalate mitigation plans for hard project dependencies
• Drive all phases of project management, including feasibility, definition, planning, prioritization, implementation, and closeout
• Apply project management methodologies and standards
• Ensure project documents are complete and up to date
• Partner cross-functionally with key stakeholders to drive project delivery
• Influence teams toward common goals despite resource and time constraints
• Create transparency and foster relationships with adjacent teams
• Build team strategy and develop and lead associates toward top performance
• Establish metrics and use data to measure operational efficiency, team health, and partnership effectiveness
• Challenge the status quo and ask tough questions

Director, Project Management – EPTech

Program Manager

• Own the day-to-day execution, annual strategy, and iteration of a Product College program
• Ensure a high-quality, associate-centric experience across the learner journey
• Identify, brief, and integrate executive product speakers, subject matter experts, and facilitators into the curriculum
• Maintain relationships with accountable executives and key stakeholders
• Analyze surveys and conduct retrospective meetings to measure impact and identify improvements
• Coordinate with data teams, consultants, and instructional designers to implement curriculum enhancements
• Manage program budgets and external vendor relationships
• Track learner data and distribution lists
• Coordinate virtual and in-person logistics across multiple locations
• Host and produce live learning sessions, including facilitator preparation, Zoom recordings, attendance tracking, and materials archiving
• Manage program communications via Slack and email
• Drive program awareness and marketing within the Capital One product community and onboarding teams
• Design, build, and implement scalable processes for reliable program delivery

Principal Associate – Program Manager, Product College

Machine Learning Engineer

• Design, build, and deliver machine learning models and components solving real-world business problems
• Collaborate with Product and Data Science teams
• Build and scale multi-tenant platforms for large-scale ML model training and serving
• Inform ML infrastructure decisions using modeling techniques and issues such as model choice, feature selection, hyperparameter tuning, dimensionality, bias/variance, and validation
• Write and test application code, develop and validate ML models, and automate tests and deployment
• Collaborate within cross-functional Agile teams to create and enhance big data and ML applications
• Retrain, maintain, and monitor production models
• Build or leverage cloud architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines feeding ML models
• Apply CI/CD practices, test automation, and monitoring for ML model and application deployment
• Manage code to reduce vulnerabilities and govern models from a risk perspective
• Apply Responsible and Explainable AI best practices
• Use programming languages including Python, Scala, or Java