AI Enablement Engineer
US Full-time Salaried 150000 - 200000 USD per-year-salaryJob Overview
Join our AI Task Force to build production AI systems that improve how we develop software and work with data. You’ll design and ship autonomous agents, tool harnesses, and intelligent workflows that solve real problems for our teams.
A core part of this role is working closely with internal teams—understanding their workflows, identifying where agents can help, and building solutions tailored to their needs. You’ll need to think critically about how LLMs actually work, what they’re good at, and where they fall short.
Our team builds with Claude Code, Cursor, and other AI-assisted development tools daily—you should be deeply comfortable in these environments and excited to push them further.
Join our AI Task Force to build production AI systems that improve how we develop software and work with data. You’ll design and ship autonomous agents, tool harnesses, and intelligent workflows that solve real problems for our teams.
A core part of this role is working closely with internal teams—understanding their workflows, identifying where agents can help, and building solutions tailored to their needs. You’ll need to think critically about how LLMs actually work, what they’re good at, and where they fall short.
Our team builds with Claude Code, Cursor, and other AI-assisted development tools daily—you should be deeply comfortable in these environments and excited to push them further.
WHAT YOU'LL DO
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Build and deploy AI agents using modern agent SDKs (Claude, OpenAI, or similar) with custom tools and function calling
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Design and build tool harnesses and execution environments for agents—both on desktop (local CLI, IDE integrations) and in the cloud (containerized, API-driven)
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Partner with internal teams across the organization to understand their workflows, identify automation opportunities, and build agents tailored to their use cases
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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
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Develop context engineering strategies—understanding how to give LLMs the right information at the right time within token limits
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Build and maintain custom tool libraries that agents can use to interact with internal systems, APIs, and data sources
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Deploy and manage agents in cloud environments with proper monitoring, error handling, and cost controls
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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
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You’ve deployed agents in cloud environments and dealt with the real-world challenges that come with it
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You’ve built tools, harnesses, or scaffolding that agents use to accomplish tasks
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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