Capital One

Financial Planning and Analysis

• Consolidate financial results across Card Servicing towers
• Report and communicate results to key business stakeholders
• Analyze performance and efficiency metrics and identify trends
• Analyze financial results, socialize drivers and impact, and provide recommendations to Card Operations business leaders
• Support building sustainable and efficient processes while integrating legacy Discover and legacy Capital One financial data and associates through automation and AI
• Prepare monthly, quarterly, and annual financial reporting and analysis
• Lead development of presentations communicating analysis results to senior audiences and leadership teams
• Analyze and explain key cost drivers and monthly variances, suggest business actions, and help manage regular reporting
• Identify and lead new analyses that generate insights into business performance and opportunities
• Partner with finance leaders, their teams, and other internal stakeholders
• Identify and drive process improvements to generate efficiencies
• Respond to ad-hoc requests and special projects

Principal Financial Analyst – Card Operations

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, large language model 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

AI Engineer – Level 4

Full-stack Engineer

• Lead a portfolio of diverse technology projects and a team of developers building distributed microservices and full-stack systems
• Create solutions that help meet 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
• 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, Agentic Orchestration

• Architect the transition from permanent access to zero standing privileges through automated access elevation and revocation
• Lead abstraction of IGA components toward a centralized, policy-based, stack-agnostic access model
• Design contextual and adaptive authorization using device posture, network risk, and user behavior telemetry
• Build developer-centric Identity-as-a-Service APIs and self-service capabilities
• Ensure IAM solutions meet stringent regulatory and audit standards in a regulated financial environment
• Define and evangelize a technical vision for the identity domain
• Decompose complex problems into practical, operational solutions
• Ensure quality of technical design and implementation
• Advise on performance, scalability, and operability
• Produce communications, code samples, blog posts, and knowledge-sharing materials
• Drive modernization from monolithic systems to microservice architectures and event-driven platforms
• Collaborate with senior engineering and architecture leaders on platform strategy and API standards
• Mentor engineers and strengthen technical expertise across engineering and product communities
• Lead cross-divisional projects promoting technology best practices and enhancements

Staff Software Engineer – Identity

General Counsel

• Provide strategic legal advice on corporate governance matters to Capital One’s Board, Board Committees, senior management, and internal clients
• Monitor governance issues, trends, and topics
• Draft Board and Committee materials on governance-related matters
• Manage director onboarding, orientation, and ongoing education programs
• Provide logistical and legal support for the Board, including agenda setting, meeting execution, minutes, resolutions, scripts, and other documents
• Manage the annual stockholder meeting and prepare related materials
• Administer corporate governance guidelines and other corporate policies
• Lead ad hoc projects with multiple stakeholders
• Manage and coordinate outside counsel when necessary
• Serve as a trusted legal advisor to the Board, its Committees, and senior management

Senior Manager, Assistant General Counsel – Governance & Securities

• 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
• Lead development and benchmarking of multi-turn conversational and tool-using agent workflows with measurable performance and safety metrics
• Implement scalable pipelines for training, fine-tuning, and deploying foundation or domain-specific models
• Collaborate with research and data engineering teams to curate datasets and improve model evaluation methodologies
• Contribute to governance and security efforts for model traceability, lineage documentation, and version control
• Mentor junior AI engineers and advocate for engineering excellence, reproducibility, and responsible experimentation

AI Engineer, Level 3

• 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, vector databases, 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

AI Engineer 5 – MLX

• Lead a portfolio of diverse technology projects and a team of developers with deep 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 supporting millions of Americans' financial empowerment
• 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 and direction across Agile teams, setting standards for design, development, and operational excellence
• Partner with architects to define service boundaries, integration patterns, and reusable frameworks
• Identify improvement opportunities, implement targeted refactoring, reduce technical debt, and enhance system performance
• Influence long-term technology strategy through evaluation of emerging tools and practices in a “you build it, you own it” environment

Full Stack Engineer – 4, Java, Python, AWS

Data Scientist

• Partner with cross-functional teams of data scientists, software engineers, and product managers to deliver customer-focused products
• Leverage Python, Conda, AWS, H2O, Spark, and other technologies to uncover insights from large volumes of numeric and textual data
• Build machine learning models through design, training, evaluation, validation, and implementation
• Translate complex data science work into tangible business goals
• Develop solutions that identify customer needs and recommend optimal solutions for financial resiliency
• Create code and models that run in analytical and production environments at scale

Senior Manager, Data Science – Resiliency Intelligence

• Lead a portfolio of diverse technology projects and direct a team of developers experienced in distributed microservices and full-stack systems
• Drive end-to-end design and implementation of large-scale, cloud-native systems with high reliability, performance, and data-design requirements
• Partner with architects and product leaders on system architecture, platform roadmaps, and shared-services strategies
• Direct the design, development, testing, and implementation of intelligent solutions using AI technologies, LLMs, and cloud infrastructure
• Partner with digital product managers to build cloud-based solutions supporting hyper-personalized user experiences
• Use Java, JavaScript, TypeScript, Python, Go, Scala, Node.js, open-source RDBMS, NoSQL databases, Docker, Kubernetes, and AWS ecosystems
• Mentor engineers, conduct code reviews, participate in technology communities, and champion modern engineering practices

Full-stack Engineer 5 – Java, Spring, Kafka, CI/CD, AI tools, Glue, RDS, DynamoDB

Human Resources

• Influence the future talent strategy of Capital One’s AI organizations
• Orchestrate an HR roadmap to scale AI/ML and build the organizational structure of the future
• Lead organizational design and talent strategy for the LOB Applied AI organization
• Develop a sustainable, aligned leadership team across AI, ML, AI/ML Product, and LOB Applied AI
• Partner with recruiting, compensation, business leaders, and HR to drive hiring strategy for scarce AI engineering and research roles
• Consult with senior executives, HR business partners, and HR partner groups to deliver aligned HR strategy
• Translate business strategy into talent strategy and reinforce an equitable associate culture
• Define talent strategy, conduct strategic workforce and succession planning, and develop recruiting and retention strategies
• Assess human capital risks, skills gaps, employment practice risks, and remediation strategies
• Provide executive coaching and advice on leadership, teamwork, organizational design, and strategy
• Lead enterprise-wide HR projects and create systemic solutions to HR challenges
• Measure HR strategy and program effectiveness using data-driven insights
• Lead and develop the HR team and partners through vision, prioritization, coaching, and development
• Oversee scaling of the Applied AI organization for 800 current FTEs and 400 projected hires
• Drive specialized talent management processes for AI-centric job families
• Operationalize the 2026 AI/ML framework and map talent to LOB goals
• Build out a novel organization while managing ambiguity and influencing undefined outcomes
• Implement hiring strategies to mitigate competitive risks, high cost-to-hire, and talent scarcity

Senior Director, HR Business Partner – Tech

DevOps Engineer

• Define the future of banking in the cloud as a senior technical expert
• Drive innovation and optimize business outcomes through strong technology solutions
• Lead technical contributions and advise on key technologies, platforms, and capability domains
• Create communications, code samples, blog posts, and other knowledge-sharing materials
• Provide technical authority and architectural vision during critical technology outages
• Lead cross-functional teams during incidents to minimize downtime
• Conduct systemic root cause analyses and resolve underlying architectural weaknesses
• Define and champion enterprise-wide standards for observability, disaster recovery, and chaos engineering
• Mentor engineering teams and help recruit and develop technical talent
• Articulate technical vision and decompose complex problems into practical operational solutions
• Ensure technical design and implementation quality
• Advise on performance, scalability, and operability
• Manage multiple projects and maximize technical impact
• 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 promoting technological best practices and enhancements
• Balance hands-on technical contribution with mentoring and elevating engineering skills

Senior Staff Engineer – Reliability Engineering

• Lead a portfolio of diverse technology projects and a team of developers with experience in distributed microservices and full-stack systems
• Create solutions that meet regulatory needs for Capital One
• 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
• 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 and shape system architecture and long-term technology direction
• Lead 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

Full-Stack Engineer

• Partner with cross-functional data scientists, software engineers, and product managers to deliver customer-focused products
• Leverage Python, Conda, AWS, H2O, Spark, and other technologies to uncover insights from large volumes of numeric and textual data
• Build machine learning models through design, training, evaluation, validation, and implementation
• Translate complex data science work into tangible business goals
• Lead and develop talent and challenge conventional thinking
• Research and evaluate emerging technologies and apply state-of-the-art methods
• Define and solve ambiguous, large-scale problems
• Develop data science solutions using open-source tools and cloud computing platforms

Senior Director, Data Scientist – Global Payment Network

• Partner with cross-functional teams of data scientists, software engineers, and product managers to deliver customer-focused products
• Leverage Python, Conda, AWS, H2O, Spark, and other technologies to uncover insights from large volumes of numeric and textual data
• Build machine learning models through design, training, evaluation, validation, and implementation
• Translate complex data science work into tangible business goals
• Lead and develop talent while challenging conventional thinking and improving the status quo
• Research and evaluate emerging technologies and apply state-of-the-art methods to data science solutions
• Develop scalable machine learning and AI solutions supporting fraud/risk, merchant acceptance, merchant intelligence, and monitoring across the payments ecosystem