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