AI Harness Engineer- Remote

What You'll Do

• Design and build secure AI infrastructure for production AI agents.
• Develop Agent Control Plane capabilities that manage, monitor, and secure agent execution.
• Build embedded controls and security mechanisms directly into AI agents.
• Develop reusable services, APIs, and developer tooling that support AI applications.
• Build and maintain integrations with modern Agent SDKs and Model Context Protocol (MCP).
• Design backend microservices using Python and AWS.
• Build observability into AI systems, including telemetry, monitoring, tracing, and operational visibility.
• Develop public-facing developer tools and command-line interfaces (CLI).
• Partner closely with AI researchers to rapidly transition prototypes into production-ready systems.
• Own projects from architecture through deployment.
• Help define engineering standards and reusable patterns across the AI platform.
• Collaborate with stakeholders across the board in our Product, Sales, and Executive teams. Along with collaborate with senior engineers while independently leading technical initiatives when appropriate

What We're Looking For

Required

• 5+ years of backend software engineering experience.
• Experience building AI Harnesses is required
• Experience with Harness & AI Security is required  
• Excellent Python development experience.
• Experience building backend services and microservice architectures.
• Experience working with AWS cloud services.
• Experience designing APIs and distributed systems.
• Hands-on experience building AI applications, AI agents, or agentic workflows.
• Experience working with Agent SDKs.
• Familiarity with Model Context Protocol (MCP).
• Experience building developer tools, APIs, or CLI applications.
• Strong understanding of software architecture and production engineering.
• Comfortable working in startup environments with significant ownership.
• Ability to independently drive projects from concept through production.

Preferred Qualifications

• Experience building Agent Control Planes.
• Experience implementing embedded controls or policy enforcement within AI agents.
• Experience building AI observability platforms.
• Security engineering background.
• Infrastructure engineering background.
• Experience building secure AI systems.
• Experience working with LLMs in production environments.
• Experience with Kubernetes and containerized services.
• Experience integrating AI systems with cloud infrastructure.
• Familiarity with AI governance, guardrails, and runtime security.