• Rapidly prototype & assess new technologies that enable hyper personalized marketing and assistive field sales systems • Partner with (& influence) our enterprise and card tech organizations to help Capital One develop the best agentic marketing & sales systems on the planet • Establish and evangelize technical charters that will help the org more quickly adopt AI technologies • Bridge the gap between our current systems and the future, ensuring that existing systems remain healthy and continue to serve the business • Regularly clear roadblocks for developers across the org - with exceptional judgement for when to apply technical, process, or risk-acceptance solutions • Serve as an authoritative expert on non-functional system characteristics, such as performance, scalability and operability • Continue learning and injecting advanced technical knowledge into our community • Act as a role model and mentor within the tech community, helping to coach and strengthen the technical expertise and know-how of our engineering and product community • Provide architectural guidance and designs for large scale cross-domain programs • Working with engineering teams to design and execute PoCs and hands-on code contribution/reviews where necessary

• Design, develop, and deploy machine learning models for production environments • Build AI-powered applications using modern software engineering best practices • Develop and optimize NLP, computer vision, predictive analytics, or Generative AI solutions • Continuously evaluate and improve model performance, scalability, and reliability • Utilize Claude Code, Codex, Anti-gravity, Zed, and other AI engineering tools to improve software development productivity • Leverage AI-assisted coding, debugging, testing, documentation, and code review workflows • Evaluate emerging AI development tools and recommend best practices for engineering teams • Build reusable AI development workflows that improve engineering efficiency • Build AI agents and intelligent automation workflows to streamline engineering and business processes • Integrate AI models into production systems using APIs and cloud platforms • Design scalable workflows that combine AI services, automation platforms, and enterprise applications • Collaborate with software engineers to embed AI capabilities into existing products • Develop AI solutions that follow secure coding, privacy, and responsible AI principles • Protect sensitive data throughout the AI development lifecycle • Identify security risks associated with AI implementations and recommend mitigation strategies • Follow best practices for secure deployment and governance of AI systems

• Design, build, and maintain automated AI evaluation pipelines for production LLM applications • Develop prompt engineering strategies and iterate on prompts and compare LLMs using quantitative evaluation methods • Build offline evaluation datasets and regression testing frameworks to measure AI performance over time • Analyze production AI behavior using Python, SQL, and statistical techniques to identify opportunities for improvement • Design experiments, A/B tests, and benchmarking methodologies for evaluating prompt and model changes • Develop dashboards and reporting that communicate AI quality, reliability, and performance metrics • Partner with engineering and product teams to safely deploy and monitor improvements to production AI systems • Investigate model failures through detailed error analysis and recommend improvements to prompts, evaluation datasets, and workflows • Help establish best practices for Responsible AI, evaluation methodologies, and continuous model improvement

• You bring deep expertise in knowledge representation, semantic systems, and applied AI, and you are energized by translating complex enterprise domains into structured, machine-understandable models. • You are comfortable working through ambiguity and building early systems that demonstrate clear value. • You own the enterprise knowledge model including entities, relationships, actions, constraints, and how they evolve over time. • You define and govern ontology standards, ensuring clear layering, reuse, and consistency across systems. • You provide technical leadership while remaining hands-on in semantic architecture and knowledge graph development. • You design and evolve enterprise knowledge graph platforms that integrate structured, semi-structured, and unstructured data, enabling reasoning, inference, and contextual retrieval. • You design the underlying data and graph architecture, including ingestion pipelines, transformation and mapping, entity resolution, schema alignment, validation, and both batch and streaming updates. • You guide key technical decisions across ontology design, graph architecture, and AI integration, including trade-offs between materialized inference, constraint validation, and query-time reasoning at scale. • You partner closely with product and engineering to ensure models are practical, scalable, and aligned to real-world use cases. • You will mentor others and help build a culture of structured thinking, semantic clarity and innovation.