• This role has been designated as ‘Remote/Teleworker’, which means you will primarily work from home. • We are seeking a dynamic, strategic, and technically fluent Sales Leader for Sovereign AI solutions to drive our go-to-market strategy across the target markets. • This leader will own order growth for Sovereign AI offerings, orchestrate sales and pursuit teams, and collaborate across business units, geographies, and delivery organizations to bring differentiated, compliant AI solutions to market. • This individual will lead a team of technologists and solution consultants and build strong internal and external followership. • Success requires deep understanding of AI technologies, regulatory landscapes, IT services delivery, and complex enterprise sales motions. • Responsible for setting the direction, managing the deliverables of the assigned group, and achieving revenue and expense objectives. • Resolves customer problems and contributes to customer satisfaction. • Manages the performance of individual contributors and the team overall and grows and develops talent within the organization.

• Build AI-Powered Developer Tools: Design, implement, and ship production-ready AI agents and tools that accelerate developer productivity such as code review and refactoring assistants, automated test generators, local environment setup tools, deployment pipeline diagnostic agents, and on-call assistance tools • Implement LLM Integrations: Build robust, production-grade integrations with LLM APIs (OpenAI, Anthropic, etc.) such as prompt engineering, response parsing, error handling, rate limiting, cost management, and performance optimization • Develop Agent Orchestration Systems: Create agent frameworks and orchestration systems that enable complex multi-step workflows, tool calling, context management, and agent-to-agent communication • Contribute to Platform Infrastructure: Build self-service platform capabilities that enable teams across Docker to rapidly deploy and operate their own AI developer tools such as deployment pipelines, observability integration, security controls, and operational tooling • Drive Adoption of AI-Native Development: Build tools and programs that accelerate adoption of AI developer tools such as Claude Code, Cursor, and Warp across Docker's engineering organization • Ensure Production Quality: Write well-tested code with strong test coverage (unit, integration, end-to-end); establish monitoring, alerting, and operational excellence for AI systems • Collaborate Cross-Functionally: Partner with Principal Engineer on architecture, work with product and design teams on features and UX, and collaborate with platform teams (Infrastructure, Security, Data) on integrations • Participate in Operations: Take part in on-call rotation for AI developer tools; respond to incidents, debug production issues, and drive continuous improvement of system reliability • Mentor and Share Knowledge: Guide other engineers through code reviews, pair programming, and technical discussions; document patterns and best practices for AI tool development • Measure and Iterate: Instrument AI tools to measure adoption, effectiveness, and developer productivity impact; iterate based on data and user feedback to continuously improve developer experience

• Build AI Developer Tool Features: Implement features for AI-powered developer tools such as code review assistants, test generators, deployment diagnostics, and on-call assistance tools • Implement LLM Integrations: Build integrations with LLM APIs (OpenAI, Anthropic, etc.) such as prompt engineering, response handling, error management, and performance optimization • Contribute to Platform Infrastructure: Help build self-service platform capabilities such as deployment pipelines, observability integration, security controls, and operational tooling that enable teams to rapidly deploy AI developer tools • Support AI-Native Development Adoption: Contribute to tools and programs that help teams adopt AI developer tools such as Claude Code, Cursor, and Warp across Docker's engineering organization • Write Quality Code: Develop well-tested code with unit and integration tests; follow team coding standards and participate actively in code reviews to learn best practices • Maintain Production Systems: Assist with monitoring, alerting, and troubleshooting production AI systems; participate in incident response and learn operational best practices • Collaborate and Learn: Work closely with Senior Engineers and Principal Engineer on technical designs; ask questions, seek feedback, and continuously improve your skills in AI/LLM technologies and platform engineering • Document Your Work: Create clear technical documentation for features you build; contribute to team knowledge base and help future team members understand systems • Participate in Team Activities: Engage in design discussions, sprint planning, retrospectives, and team activities; contribute ideas for improving developer tools and team processes • Grow Your Expertise: Continuously learn about AI/ML technologies, developer tooling best practices, and platform engineering patterns through hands-on work and mentorship from experienced engineers

• Build and Scale the AI Developer Tools Team: Hire, onboard, and develop a high-performing engineering team from the ground up with expertise in AI/LLM integration, platform engineering, and developer experience; establish team culture, technical standards, and operating norms • Ship AI-Powered Developer Tools: Deliver production-ready AI agents for developer productivity (automated PR reviews, code generation, documentation), observability insights (anomaly detection, root cause analysis), and operational automation (deployment pre-reviews and failure investigations, incident response assistance) • Build Self-Service AI Developer Tools Platform: Create and operate the foundational infrastructure that enables teams across Docker to build, deploy, host, and scale their own AI developer tools including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, cost management, and operational tooling • Drive Platform Adoption and Developer Experience: Establish the AI Developer Tools platform as the default path for teams building AI-powered tooling; deliver self-service capabilities, comprehensive documentation, templates, and best practices that reduce time-to-production from weeks to days • Partner on AI Strategy and Technology: Work closely with engineering leadership and Agent Dev technical leadership to align on AI technology choices, architectural patterns, and integration strategies; stay current on LLM advancements and developer tooling trends • Explore Productization Opportunities: As internal AI developer tools demonstrate value, partner with product management and go-to-market teams to evaluate commercial viability; prototype, validate, and transition successful internal tools into customer-facing product offerings • Deliver Measurable Impact: Define and track success metrics for both AI developer tools (adoption rates, productivity gains, time saved) and platform capabilities (time-to-deploy new tools, number of teams using platform, operational efficiency) • Cross-Functional Collaboration: Partner with product engineering teams (Hub, Registry, Cloud/AI, Scout, Accounts & Billing), platform teams (Infrastructure, Security, Data), and product leadership to understand requirements, gather feedback, and align on priorities • Operational Excellence: Ensure reliability, security, and performance of AI developer tools and the hosting platform; establish SLOs, monitoring, and incident response for production AI systems serving internal developers • Team Development and Culture: Mentor and grow engineers on your team; foster a culture of experimentation, rapid prototyping, and learning; attract diverse talent excited about AI and developer experience