Senior Software Engineer, AI Developer Tools

Docker, Inc

. Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation .

Posted 10/14/2026full-timeRemote • United StatesSenior💰 $160,900 - $260,700 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating production AI systems, with a strong focus on LLM-powered applications, developer tools, and cloud-native infrastructure. Proficient in integrating AI technologies into CI/CD pipelines and observability platforms while ensuring reliability, security, and performance standards.

Highest-signal resume keywords

LLM-Powered Application DevelopmentCloud-Native InfrastructureCI/CD Pipeline IntegrationGo Programming LanguageDeveloper Productivity Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringLLM APIsPrompt EngineeringDistributed SystemsKubernetesTerraformPulumiGitOpsArgoCDPython

Soft Skills

Clear CommunicationOwnership MentalityAutonomous WorkProduct Judgment

Tools & Technologies

GrafanaMCP ServersAI Agent FrameworksObservability SystemsIncident Response Tools

Industry Keywords

DevOpsSREPlatform EngineeringAI-Assisted DevelopmentDeveloper Tools

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformGrafanaJavaKubernetesOpen SourcePythonRustTerraformGo

About the role

Key responsibilities & impact

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
Build systems that help Docker engineers adopt AI-native development tools
Build integrations between AI developer tools and CI/CD pipelines, observability platforms, and deployment systems
Instrument tools to measure adoption, effectiveness, and developer satisfaction
Build and improve self-service platform capabilities including ArgoCD/GitOps deployment pipelines, Grafana observability integration, security controls, and cost management
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 through production
Write design documents and participate in design reviews
Review code and share knowledge with the team
Participate in the on-call rotation and improve alerts, runbooks, and tooling
Follow AI-assisted engineering practices and validate AI-generated output
Collaborate with Infrastructure, Security, Data, product engineering, and platform teams
Help newer engineers learn the team's tools, patterns, and practices
Track emerging AI/LLM technologies and agent frameworks and share useful findings

Requirements

What you’ll need

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 including OpenAI and Anthropic
Knowledge of prompt engineering, agent orchestration frameworks, and practical AI applications in software development workflows
Experience building and operating scalable backend services, distributed systems, or developer-facing platforms
Experience with cloud-native infrastructure, Kubernetes, CI/CD pipelines, and observability systems
Proficiency in Go, Rust, Java, or Python; Go preferred
Strong software engineering fundamentals
Experience building developer tools, platform systems, or internal tools used by other engineers
Good product judgment and ability to evaluate developer experience and technical trade-offs
Clear written and spoken communication suited to a remote, async-first company
Ownership mentality and 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
Experience with MCP or similar AI agent integration standards preferred
Background in developer productivity, DevOps, SRE, or platform engineering preferred
Contributions to open source AI tools, developer tooling, or platform engineering projects preferred
Experience with GitOps deployment patterns such as ArgoCD or Grafana-based observability preferred
Familiarity with security and operational practices for production AI systems preferred
Experience with infrastructure-as-code such as Terraform or Pulumi and major cloud platforms such as AWS, GCP, or Azure preferred
Must be legally authorized to work in the United States

Benefits

Comp & perks

Equity for all full-time employees
Remote-first work from home
Flexible scheduling
Generous PTO
Quarterly Whaleness Days
End-of-year Whaleness break
Home office support
Technology stipend equivalent to US$100 net per month
Annual learning and development stipend for conferences, courses, certifications, and continued learning
16 weeks of paid parental leave after six months of employment
Medical benefits
Retirement benefits
Paid holidays
Docker swag