Software Engineer III, AI Developer Tools

Docker, Inc is a company focused on making app development easier for developers. They are seeking a Software Engineer III to join their AI Developer Tools team, where the role involves building AI-powered tools that enhance developer productivity and transform how developers work with code and applications.

Responsibilities:

• 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
• Take part in on-call rotation for your team; respond to incidents, debug production issues, and drive continuous improvement of system reliability

Requirements:

• 4+ years building production-grade backend systems or developer-facing tools with strong software engineering fundamentals
• Hands-on production experience with AI/ML technologies including practical experience with LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, and AI agent development
• Proficiency in Go (preferred), Rust, Java, or Python with strong software engineering fundamentals
• Experience designing and building distributed systems, microservices, or platform infrastructure
• Strong understanding of cloud-native systems (AWS, GCP, or Azure), APIs, and data stores
• Solid grasp of CI/CD, automated testing, code review practices, and modern development workflows
• Demonstrated ability to work independently on day-to-day work with general guidance on new projects
• Product-minded approach to building developer tools with focus on user experience and measurable outcomes
• Excellent communication skills in remote, asynchronous environments with ability to document technical decisions clearly
• Ability to build effective working relationships across multiple teams
• Ownership mentality with bias for action and iterative delivery
• Comfortable working autonomously across distributed teams and navigating ambiguity
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• Contributions to open source AI tools, developer tooling, or platform engineering projects
• Experience with MCP (Model Context Protocol) or similar AI agent integration standards
• Background in developer productivity, DevOps, SRE, or platform engineering domains
• Experience with Kubernetes, Docker, and container orchestration
• Knowledge of developer tools ecosystems (IDEs, CI/CD platforms, observability tools)
• Experience with infrastructure-as-code (Terraform, Pulumi) and GitOps deployment patterns (ArgoCD, FluxCD)
• Understanding of security, compliance, and operational best practices for production AI systems
• Understanding of software design patterns and distributed systems principles