AI engineer in content quality

As an AI Engineer for Content Quality, you will collaborate with engineers to build, execute, and iterate on agent orchestrations, automated workflows, and system integrations that power content quality processes. This role focuses heavily on Model Context Protocol (MCP) integrations, agent skills, AI orchestration, and Google’s internal infrastructure.

• Build and improve AI agent orchestrations that automate content quality workflows and reduce the need for manual intervention.
• Develop automated workflows that leverage LLMs, tools, and agent chaining.
• Collaborate with engineers and domain specialists to design and refine workflow logic.
• Create and maintain clear workflow documentation so domain specialists can understand, tune, and improve processes.
• Iterate on workflows based on performance, quality, and operational feedback.
• Develop integrations between AI models and external platforms using Model Context Protocol (MCP) servers and similar frameworks.
• Build primarily in Python, with an emphasis on reliable and maintainable integrations.
• Work with APIs, HTTP, REST, and client-server architectures.
• Implement robust error handling, retries, and failure recovery to ensure reliable integrations.
• Maintain and update integrations as underlying systems, APIs, and tools evolve.
• Develop and maintain agent skills and tool integrations.
• Use CLI-based development tools, including the Gemini CLI, to improve development, debugging, and testing efficiency.
• Implement monitoring and audit logging to ensure agent actions are traceable, observable, and queryable.
• Help build reliable systems that can be monitored and maintained at scale.

• Proficiency in Python.
• Basic understanding of SQL and the ability to write simple queries.
• API integrations
• HTTP and RESTful APIs
• Client-server networking
• LLM APIs
• Prompt engineering
• Structured outputs
• Tool use and agent chaining
• Understanding of AI/ML frameworks and emerging protocols such as Model Context Protocol (MCP).
• Ability to understand technical specifications and translate them into reliable implementations.
• Experience with, or a strong willingness to learn, Google internal tools such as: PLX, Buganizer, Jet Ski
• Comfortable working with CLI-based development tools, including the Gemini CLI.
• Willingness to learn new internal infrastructure and development environments quickly.
• Comfortable working from a provided technical specification and implementing solutions accurately with guidance from senior engineers.
• Strong attention to detail and commitment to code and workflow quality.
• Ability to balance speed, quality, and reliability in a fast-changing environment.
• Strong communication and collaboration skills.
• Clear, structured thinking and strong problem-solving abilities.
• Self-motivated and eager to learn and grow in the field of AI engineering.
• Comfortable working in a collaborative environment and incorporating feedback from engineers and domain specialists.

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