Senior Software Engineer, AI Developer Tools

Docker is hiring a Senior Software Engineer for its AI Developer Tools team. The role builds and operates AI-powered tools that improve developer productivity, along with a self-service platform that enables teams to prototype, deploy, and scale their own AI tools.

Responsibilities:

• Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation
• Integrate LLM providers, agent orchestration frameworks, and MCP servers into reliable tools, following the team's patterns and standards
• Build systems that help Docker's engineers adopt AI-native development tools
• Build integrations between AI developer tools and existing developer infrastructure such as CI/CD pipelines, observability platforms, and deployment systems
• Instrument your tools so the team can measure adoption, effectiveness, and developer satisfaction
• Build and improve platform capabilities, including deployment pipelines (ArgoCD/GitOps), observability integration (Grafana), security controls, and cost management, that help teams ship their own AI tools faster
• Help reduce time-to-production for new products from weeks to days
• Contribute to reliability, security, and performance standards for AI systems, including SLOs, monitoring, and incident response
• Take features and services from requirements to production. Ask about scale, users, and constraints before designing, and write down your assumptions
• Write clear design documents for the work you own, and participate in design reviews for the team
• Review code thoroughly and share what you learn with the rest of the team
• Take part in the team's on-call rotation, and help improve alerts, runbooks, and tooling so the next incident is easier
• Follow the AI-assisted engineering practices the team sets: use AI tools critically, validate their output, and stay accountable for what you ship
• Work with the Senior Manager and the team's Principal and Staff engineers to understand priorities and shape your work
• Partner with Infrastructure, Security, and Data teams on integrations and shared standards
• Help newer engineers get up to speed on the team's tools, patterns, and practices
• Keep up with emerging AI/LLM technologies and agent frameworks, and bring useful findings back to the team

Requirements:

• 6+ years of software engineering experience building and operating production systems
• Hands-on experience building LLM-powered applications, AI agents, or AI-assisted developer tools
• Working knowledge of LLM APIs (OpenAI, Anthropic, and others), prompt engineering, agent orchestration frameworks, and practical uses of AI in software development workflows
• Experience building and operating scalable backend services, distributed systems, or developer-facing platforms
• Experience with cloud-native infrastructure, including Kubernetes, CI/CD pipelines, and observability systems
• Proficiency in Go (preferred), Rust, Java, or Python, with strong software engineering fundamentals
• Experience building developer tools, platform systems, or internal tools that other engineers rely on
• Good product judgment: you weigh developer experience and technical trade-offs, and you ask clarifying questions before committing to a design
• Clear written and spoken communication suited to a remote, async-first company
• Ownership mentality with a bias for action and iterative delivery in ambiguous, fast-moving environments
• Comfortable working autonomously in a distributed, remote-first team
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• Docker considers visa sponsorship on a case-by-case basis based on business needs
• Go
• Experience with MCP (Model Context Protocol) or similar AI agent integration standards
• Background in developer productivity, DevOps, SRE, or platform engineering
• Contributions to open source AI tools, developer tooling, or platform engineering projects
• Experience with GitOps deployment patterns (ArgoCD) or Grafana-based observability
• Familiarity with security and operational practices for production AI systems
• Experience with infrastructure-as-code (Terraform, Pulumi) and major cloud platforms (AWS, GCP, Azure)