• 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 agents that simplify on-call tasks when handling incidents • 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 and Senior Engineers on architecture, work with product and design teams on features and UX, and collaborate with platform teams (Infrastructure, Security, Data) on integrations; build effective partnerships across multiple teams • Act as Technical Resource: Help teammates solve problems and share knowledge through code reviews and technical discussions • 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 • Document and Share: Create clear technical documentation for features you build; share patterns and learnings with the team • 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 and operate production-ready LLM-powered services end-to-end • Develop fast, reliable AI systems that power core platform features • Enable accurate, data-grounded recommendations and intelligent workflows • Contribute across multiple projects built on the AI engineering stack • Support the personal and professional growth of team members by having access to an executive coach

• Collaborate with cross-functional teams to design and build multi-agent workflows • Engage with clients to define requirements and oversee software development lifecycle • Implement evaluation pipelines to measure agent performance • Manage backend tasks and contribute to frontend development • Stay informed about advancements in AI

• Collaborate with the CTO, AI consultant, and development team to design and deploy robust, scalable AI/ML architectures • Develop, train, and maintain AI models, data pipelines, and integrations with APIs, databases, and SaaS platforms • Experiment with new LLMs, embeddings, vector databases, and orchestration frameworks (e.g., LangChain, Semantic Kernel, or similar) • Lead hands-on development/ prototyping of LLM-based applications, intelligent agents, and automation workflows • Define AI architecture standards, establish model governance, and contribute to the long-term AI strategy in collaboration with the CTO • Partner with business and technical leaders to translate strategic AI goals into actionable engineering initiatives • Design systems for high availability, scalability, and efficient deployment in cloud environments (Azure, AWS, or GCP) • Create technical documentation, architecture diagrams, and playbooks for deployment and scaling of AI solutions • Participate in code reviews, mentor junior developers, and foster a culture of innovation and excellence in AI engineering • Stay at the forefront of emerging AI frameworks, APIs, and tools, bringing forward practical innovations to enhance products and systems