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

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.
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.

Benefits

Work Model – Remote
Employment Type - Full-time

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI/ML technologiesLLM APIsGoRustJavaPythondistributed systemsmicroservicesCI/CDKubernetes

Soft Skills

excellent communicationownership mentalitybias for actioniterative deliveryability to document technical decisionscomfortable working autonomouslynavigating ambiguitymentoringcollaborationcross-functional teamwork

Certifications

Bachelor’s degree in Computer ScienceBachelor’s degree in Engineeringrelated field degree