Principal AI System Engineer - Python

As a Principal AI Systems Engineer at our company, you will play a crucial role in architecting, building, and owning AI systems that automate expert-intensive technical workflows end-to-end. Your responsibilities will include solving real business problems with AI, ensuring full implementation and adoption of solutions, and measuring their effectiveness. Here's what you will be doing: - Own end-to-end architecture of AI automation systems, including workflow decomposition, component communication, human checkpoints, and failure behavior. - Design and build internal CLI frameworks, reusable libraries, and agent scaffolding. - Author and maintain agent instruction files and MCP server definitions. - Configure CLI environments and engineering standards. - Evaluate and document architectural trade-offs across reliability, latency, cost, and maintainability. You will also be responsible for: - Building production-grade AI pipelines in Python for orchestration, context assembly, schema validation, and retry strategies. - Integrating AI systems with external tooling such as version control, build pipelines, SDKs, and compliance. - Designing, versioning, and maintaining system prompts and agent instructions as engineering artifacts. - Implementing full audit trails, enforcing versioning of agent instructions, and setting technical standards for AI development across the organization. To be successful in this role, you should have: - Proven track record of building production AI automation systems from scratch. - Hands-on expertise with Claude Code, Codex CLI, Cursor, or equivalent tools. - Experience in designing and deploying MCP servers and custom tools. - Proficiency in building internal CLI frameworks, agent scaffolding, and reusable libraries. - Experience in creating internal tooling and automation to improve engineering team efficiency. - Deep prompt and context engineering skills. - Proficiency in LLM orchestration frameworks and AI evaluation frameworks. - Production Python engineering skills with cloud platform experience. Bonus points for experience in integrating AI systems with external APIs. Join us in our mission to drive AI innovation and automation. As a Principal AI Systems Engineer at our company, you will play a crucial role in architecting, building, and owning AI systems that automate expert-intensive technical workflows end-to-end. Your responsibilities will include solving real business problems with AI, ensuring full implementation and adoption of solutions, and measuring their effectiveness. Here's what you will be doing: - Own end-to-end architecture of AI automation systems, including workflow decomposition, component communication, human checkpoints, and failure behavior. - Design and build internal CLI frameworks, reusable libraries, and agent scaffolding. - Author and maintain agent instruction files and MCP server definitions. - Configure CLI environments and engineering standards. - Evaluate and document architectural trade-offs across reliability, latency, cost, and maintainability. You will also be responsible for: - Building production-grade AI pipelines in Python for orchestration, context assembly, schema validation, and retry strategies. - Integrating AI systems with external tooling such as version control, build pipelines, SDKs, and compliance. - Designing, versioning, and maintaining system prompts and agent instructions as engineering artifacts. - Implementing full audit trails, enforcing versioning of agent instructions, and setting technical standards for AI development across the organization. To be successful in this role, you should have: - Proven track record of building production AI automation systems from scratch. - Hands-on expertise with Claude Code, Codex CLI, Cursor, or equivalent tools. - Experience in designing and deploying MCP servers and custom tools. - Proficiency in building internal CLI frameworks, agent scaffolding, and reusable libraries. - Experience in creating internal tooling and automation to improve engineering team efficiency. - Deep prompt and context engineering skills. - Proficiency in LLM orchestration frameworks and AI evaluation frameworks. - Production Python engineering skills with cloud platform experience. Bonus points for experience in integrating AI systems with external APIs. Join us in our mission to drive AI innovation and automation.