Senior Software Engineer, AI Developer Tools

Decision Foundry•Seattle, WA

5d•Remote

About The Position

We are seeking a Senior Software Engineer to join the new AI Developer Tools team at the forefront of AI-powered developer productivity for our client who will build cutting-edge AI agents and tools that transform how developers write code, debug issues, deploy applications, and respond to incidents both internally and for the customers worldwide. This is an opportunity to work at the intersection of AI and developer experience, building production systems that leverage LLMs and AI agents to accelerate developer workflows. You'll architect and implement AI-powered tools such as code review assistants, automated test generators, deployment diagnostics agents, and on-call assistance tools. You'll also contribute to the self-service platform that enables teams across to rapidly build and deploy their own AI developer tools. Your work will directly impact how engineers build and operate services powering 20 million users, and as these tools mature, you'll help transform them into commercial offerings for the customers. You'll collaborate closely with the Principal Engineer on technical architecture, partner with product and design teams on user experience, and work autonomously in a fast-paced, remote-first environment where rapid iteration and shipping are core values. What Would Make Someone Successful In This Role You're passionate about developer experience and excited by the potential of AI to transform how developers work. You have hands-on experience building production systems with LLMs or AI agents, and you understand the nuances of prompt engineering, agent orchestration, and evaluating AI system effectiveness. You think in terms of products and platforms, balancing technical excellence with pragmatism to ship iteratively while maintaining high quality bars. You're comfortable navigating the rapidly evolving AI/LLM landscape, experimenting with new tools and approaches, and making pragmatic technology choices. You thrive in ambiguity, enjoy rapid prototyping, and have strong opinions on what makes developer tools that engineers love to use. Most importantly, you're collaborative, communicate clearly in remote environments, and take ownership of your work from design through deployment and operations.

Requirements

6+ years building production-grade backend systems or developer-facing tools.
Hands-on experience with AI/ML technologies such as practical production experience with LLM APIs (OpenAI, Anthropic, etc.), prompt engineering, or 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.
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.
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.

Nice To Haves

Experience with AI agent frameworks (LangChain, LangGraph, CrewAI, or similar).
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, 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.

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 on-call assistance tools
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 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 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 on architecture, work with product and design teams on features and UX, and collaborate with platform teams (Infrastructure, Security, Data) on integrations
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
Mentor and Share Knowledge: Guide other engineers through code reviews, pair programming, and technical discussions; document patterns and best practices for AI tool development
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
On-Call Rotation: Take part in on-call rotation for your team; respond to incidents, debug production issues, and drive continuous improvement of system reliability