Full Stack AI Engineer

Role overview Join a team deploying production AI systems directly inside client operations. The role pairs with solutions architects to translate detailed strategy documents into deployed software—covering backend services, agent orchestration, integrations, and the interfaces operators rely on daily. The work is hands-on engineering with ownership that runs from initial build through ongoing monitoring and iteration.

Responsibilities - Design and ship production AI systems end-to-end, spanning backend services, agent orchestration, and operator-facing user interfaces. - Operate within an internal agentic build harness, contributing to spec-driven development and test-driven validation practices. - Translate detailed strategy specifications into deployable systems that hold up against real operational data and workflows. - Own systems after launch, including deployment, monitoring, and iterative improvement cycles. - Contribute to the internal tooling and harness that powers the team's "dark factory" approach to AI development.

Requirements - Strong full-stack engineering background with Python and TypeScript, modern web stacks, and cloud infrastructure. - Production-grade experience building with large language models, agent frameworks, and adjacent AI tooling. - Comfort navigating ambiguity, working spec-first, and operating in fast iteration cycles. - A bias for shipping systems that perform in real operations rather than polished demos.

Nice to have - Background in retrieval systems, evaluation pipelines, voice agents, or vertical-specific integrations such as CRM, EHR, or ERP platforms.

Benefits and work setup Competitive compensation, a fully remote and async-friendly team structure, and exposure to mid-market and enterprise operators across more than ten industries. The role offers direct involvement in building the proprietary tooling that shapes how AI gets shipped at scale.