Applied AI Engineer

Responsibilities

• AI Discovery & Strategy
• Socialize AI capabilities, limitations, and roadmap across the org, acting as an internal resource and thought leader on what's possible.
• Maintain a prioritized backlog of AI opportunities across the business, triaging ideas from leadership, product, and operations into scoped, deliverable workstreams.
• Define AI initiatives end-to-end: problem framing, data requirements, build vs. buy decisions, and measurable success criteria.
• Stay current on the AI tooling landscape and recommend adoption where it meaningfully improves velocity, quality, or competitive positioning.
• Building & Delivery
• Build, integrate, and maintain AI-powered features and workflows, leveraging LLM APIs (OpenAI, Anthropic, etc.), RAG pipelines, and agentic frameworks.
• Develop automation workflows using low/no-code orchestration tools (n8n, Make) to deliver AI solutions rapidly without requiring full engineering cycles.
• Integrate AI capabilities into existing systems and codebases in close partnership with the product and engineering teams.
• Own the full AI development lifecycle, including prototyping, evaluation, deployment, monitoring, and iteration, with support from engineering, product, and operational teams.

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