PRINCIPAL APPLIED AI ENGINEER

About the role

A fast-growing, privacy-first consumer AI company — where millions of people and AI agents interact on a private, uncensored foundation — is hiring a Principal Applied AI Engineer to build the internal platform that puts AI agents to work across the business. Revenue is accelerating and headcount is climbing, but instead of hiring to patch every bottleneck, the company wants to multiply each team's output with agent tooling. You'll own the foundational infrastructure — agent templates, tool libraries, retrieval systems, and no-code abstractions — that lets non-engineers in marketing, support, and finance build their own agents, all running on the company's own inference API and models so nothing leaks to third parties. It's a high-autonomy, ship-fast mandate in a domain with no established playbook.

What you'll do

• Build the agent platform — design and deploy the foundational infrastructure that lets every team create, deploy, and manage internal AI agents, including templates, tool libraries, retrieval systems, and abstractions usable by non-engineers.
• Own build-vs-buy — evaluate commercial agent platforms and frameworks and decide what to build in-house versus orchestrate with existing tools.
• Run on in-house inference — ensure the internal platform runs on the company's own inference API and models, using it as a live proving ground for the core product.
• Architect for privacy — design systems that preserve data privacy and prevent internal data from leaking to third-party providers.
• Abstract for non-engineers — create usable abstractions and UX layers so marketing, support, and finance teams can build and operate agents on their own.
• Partner across the org — work alongside teams across the company to understand their workflows and pain points, and turn them into platform capabilities.
• Measure impact and ROI — instrument the platform with production traces and evaluations to quantify each agent's impact and return.
• Enable and educate — drive adoption through workshops, documentation, and proof-of-concept demos that show teams what's possible.

What we're looking for

• Senior software engineer with production-systems experience, fluent in at least two of Rust, TypeScript, Go, and Python.
• Hands-on production experience with LLM APIs, agent orchestration, tool calling, and RAG.
• A platform-engineer mindset — you build the infrastructure and abstractions other teams build on top of.
• Comfortable with ambiguity in an emerging domain that has no established best practices.
• Treat data privacy as a system-design problem, not an afterthought.
• Skilled at building products and interfaces for non-technical users.
• Pragmatic about build-versus-buy trade-offs.
• Bias toward rapid iteration and shipping working prototypes over exhaustive documentation.

Bonus points

• Experience building internal developer platforms or agent frameworks adopted by non-engineers.
• Familiarity with self-hosted or privacy-preserving model inference.

What success looks like

Within your first months, non-engineering teams are independently building and shipping agents on infrastructure you designed — running on in-house inference, private by default — and you can point to measurable productivity gains across departments.