AI Developer

About The Company Docker, Inc. has established itself as a leading innovator in the developer tooling space, trusted by over 20 million monthly users worldwide. Renowned for its containerization technology, Docker enables developers and organizations to build, share, and run applications seamlessly across diverse environments. With more than 20 billion container image pulls, Docker's platform is integral to modern software development and deployment workflows. The company operates as a globally distributed, remote-first organization, committed to empowering developers through cutting-edge tools such as Docker Desktop, Docker Hub, and Docker Scout. As the landscape of software development evolves with the advent of AI, Docker is at the forefront, providing secure, sandboxed environments, verified images, and infrastructure that facilitate autonomous workflows. Docker's mission is to redefine how software gets built and delivered, fostering innovation and efficiency across the tech industry. About The Role Docker is seeking a highly skilled and motivated Senior Software Engineer to join its AI Developer Tools team. This pivotal role involves developing AI-powered developer tools and the underlying platform that supports them. The successful candidate will operate at the intersection of artificial intelligence, developer experience, and platform engineering, contributing to the creation of tools that enhance developer productivity and streamline AI integration within Docker’s ecosystem. The role encompasses designing, building, and maintaining advanced AI agents, such as code review assistants, automated testing tools, deployment diagnostics, and incident response automations. Additionally, the engineer will contribute to a self-service platform infrastructure that enables product and platform teams across Docker to prototype, build, and deploy their own AI tools efficiently. Reporting to the Senior Manager of AI Developer Tools, this position offers an exciting opportunity to influence the future of AI-driven development workflows, working closely with principal engineers, product teams, and platform specialists in a collaborative, innovative environment. Qualifications The ideal candidate will possess over six years of experience in software engineering, particularly in building and operating scalable, production-grade systems. Hands-on experience with large language models (LLMs), AI agents, and AI-assisted developer tools is essential. Proficiency with LLM APIs from providers such as OpenAI and Anthropic, along with expertise in prompt engineering and agent orchestration frameworks, is highly desirable. The candidate should have a solid background in developing backend services, distributed systems, and developer-facing platforms, with practical knowledge of cloud-native infrastructure, including Kubernetes, CI/CD pipelines, and observability tools. Strong programming skills in Go (preferred), Rust, Java, or Python are required, complemented by experience in building internal tools or developer platforms relied upon by engineering teams. Excellent communication skills, a product-oriented mindset, ownership mentality, and the ability to work autonomously in a distributed environment are crucial. A bachelor’s degree in Computer Science, Engineering, or related fields, or equivalent practical experience, is necessary. Preferred qualifications include familiarity with MCP (Model Context Protocol), contributions to open-source AI projects, and experience with infrastructure-as-code tools and cloud platforms such as AWS, GCP, or Azure. Responsibilities Building and Operating AI Developer Tools Design, develop, and maintain production AI agents aimed at enhancing developer productivity, including code review assistants, automated test generators, deployment diagnostics, and incident response automation tools.Integrate various AI services, such as LLM providers and agent orchestration frameworks, into reliable, scalable tools following established patterns and standards.Create systems that facilitate the adoption of AI-native development practices among Docker engineers.Develop integrations between AI developer tools and existing developer infrastructure, including CI/CD pipelines, observability platforms, and deployment systems.Implement instrumentation to measure adoption rates, effectiveness, and developer satisfaction to inform continuous improvement. Self-Service Platform Development Enhance platform capabilities, including deployment pipelines (ArgoCD/GitOps), observability (Grafana), security controls, and cost management features, enabling teams to deploy their own AI tools more rapidly.Reduce the time-to-production for new features and tools from weeks to days by streamlining processes.Maintain high standards for reliability, security, and performance, establishing clear SLOs, monitoring, and incident response protocols. Design and Delivery Translate requirements into scalable, efficient features, considering scale, user needs, and operational constraints.Draft detailed design documents and actively participate in design reviews.Conduct thorough code reviews, sharing insights and best practices with team members.Participate in the on-call rotation, contributing to alerting, runbooks, and tooling improvements to facilitate incident management.Adopt AI-assisted engineering practices responsibly, validating AI outputs and maintaining accountability for shipped features. Collaboration and Growth Collaborate with leadership and cross-functional teams to align priorities and define work scope.Partner with Infrastructure, Security, and Data teams to ensure standards and integrations are robust and secure.Mentor and onboard new engineers, fostering knowledge sharing and team growth.Stay abreast of emerging AI and LLM technologies, bringing innovative ideas and insights to the team. Benefits Docker offers a comprehensive benefits package designed to support its remote-first workforce. Employees enjoy flexible working arrangements, allowing them to work from home or from Docker’s offices in Seattle and Paris for in-person collaboration. Competitive compensation is complemented by equity options