AI Product Engineer

WHAT YOU'LL DO

• Build and deploy AI agents using modern agent SDKs (Claude, OpenAI, or similar) with custom tools and function calling

• Design and build tool harnesses and execution environments for agents—both on desktop (local CLI, IDE integrations) and in the cloud (containerized, API-driven)

• Partner with internal teams across the organization to understand their workflows, identify automation opportunities, and build agents tailored to their use cases

• Think critically about LLM capabilities and limitations—understand the differences between models, when to use which, and how to get the best results from each

• Develop context engineering strategies—understanding how to give LLMs the right information at the right time within token limits

• Build and maintain custom tool libraries that agents can use to interact with internal systems, APIs, and data sources

• Deploy and manage agents in cloud environments with proper monitoring, error handling, and cost controls

• Optimize LLM costs and performance through prompt engineering, caching, and smart model selection

WHO YOU ARE

• You’ve built AI agents and shipped them to production—not just prototypes

• You’ve deployed agents in cloud environments and dealt with the real-world challenges that come with it

• You’ve built tools, harnesses, or scaffolding that agents use to accomplish tasks

• You use Claude Code and Cursor daily—you’re deeply comfortable with AI-assisted development, including headless mode, multi-file editing, and MCP server integration

• You think critically about LLMs—you understand how they work under the hood, not just how to call an API
• You understand the differences between models (Claude, GPT, Gemini, open-source) and can reason about which to use for a given task
• You have strong product sense—you focus on what users actually need, not just what’s technically interesting
• You’re pragmatic—you ship 80% solutions quickly and iterate based on feedback
• You can sit with a non-technical team, understand their pain points, and translate that into an agent that actually helps
• You take ownership and drive things from idea to measurable impact
• You communicate clearly—you can explain complex AI systems to anyone in the company
• You stay current with the rapidly evolving AI landscape and bring new ideas to the team
• You’re comfortable working across cloud platforms (GCP, AWS, Azure) and containerized environments
• Experience with advanced agent patterns or multi-agent systems
• Experience building and configuring MCP (Model Context Protocol) servers
• Open-source contributions to AI/ML projects
• Familiarity with observability tools for LLM applications
• Media, ad tech, or streaming data domain knowledge