• Own the vision, strategy, and delivery of AI-led solutions that materially improve Chainguard’s go-to-market effectiveness across Sales, Marketing, Customer Success, and Revenue Operations. • Identify, prioritize, and deliver high-impact AI use cases across the GTM lifecycle, including pipeline intelligence, deal prioritization, forecasting, personalization, sales enablement, RFP, and security questionnaire automation, customer insights, and retention signals. • Act as the AI product owner for GTM, translating business problems into scalable AI solutions that drive measurable outcomes such as revenue growth, productivity gains, and improved customer experience. • Lead the design and deployment of GenAI and predictive ML applications, including internal AI assistants, recommendation systems, and decision-support tools embedded into GTM workflows and systems. • Partner closely with GTM leadership, RevOps, GTM Systems, Data, Security, IT, and Legal to ensure AI solutions are trusted, secure, explainable, and compliant. • Establish best practices for the full AI solution lifecycle—from data sourcing and model development to deployment, monitoring, iteration, and adoption. • Drive data-driven decision making by ensuring AI solutions are grounded in high-quality data, clear success metrics, and executive-ready insights. • Stay ahead of emerging AI technologies and vendors, making informed build vs. buy vs. partner decisions aligned to Chainguard’s strategy and security posture. • Build, mentor, and scale a high-performing team of AI engineers, data scientists, and solution architects, fostering a culture of innovation, ownership, and impact. • Serve as a trusted advisor and thought partner to executives, clearly communicating AI strategy, tradeoffs, and business value.

• Define and own Coursedog’s enterprise AI strategy, aligned to company priorities and business outcomes • Build and maintain a multi-year roadmap for AI adoption, innovation, and governance • Partner with senior leadership to translate business needs into prioritized AI initiatives and secure alignment and buy-in • Track industry trends and emerging technologies to inform strategic direction and investment decisions • Build and own Coursedog’s AI Central Operating System, connecting agents, data, and workflows to enable cross-functional automation and decision-making • Oversee the design, deployment, and scaling of AI applications, agents, and integrations across the organization • Establish standards for AI integration, data usage, security, and safe use across tools and vendors • Lead vendor selection, tool rationalization, experimentation, and scaling of high-impact AI solutions • Own the AI enablement operating rhythm, including roadmap execution, learning content, internal documentation, and feedback loops • Build AI fluency across the organization through onboarding, workshops, playbooks, and function-specific best practices • Lead a cross-functional AI Scrum Team to develop reusable assets, guidance, and support for teams • Partner with functional leaders to embed AI into workflows, KPIs, and operating playbooks • Define and track AI adoption, ROI, productivity gains, and business outcomes • Translate complex technical concepts into clear, actionable insights for executives and stakeholders • Champion AI adoption through storytelling, executive briefings, and visible wins • Maintain reporting and feedback loops to continuously refine AI priorities and maximize impact

• Develop and evaluate a real-world AEC design use case, supporting design decisions through a collaborative, multi-agent AI workflow • Iteratively prototype, test, and refine AI-driven workflows, documenting both successes and failures • Build and maintain code repositories, along with clear documentation and compelling visual outputs • Identify and define new use cases and assess industry applicability of developed solutions • Communicate research outcomes through internal presentations at the conclusion of the internship • Contribute to or lead the preparation of a peer-reviewed publication for an external AI and/or AEC conference

• Experience and research AI workloads and DL models specifically tailored for large-scale deep learning LLM training on NVIDIA supercomputers with a focus on High-performance networking. • Benchmarking, Profiling, and Analyzing the performance to find bottlenecks and identify areas of improvement and optimizations, with a strong emphasis on networking aspects. • Implement performance analysis tools. • Collaborating with many teams from HW to SW to provide performance analysis insights. • Define performance test planning, set performance expectations for new technologies and solutions, and work to reach the performance targets limits.