AI Engineer (Enterprise Systems)

About the Role As an AI Engineer on our Enterprise Systems team, you will be embedded directly in the trenches of our most strategic enterprise accounts. You'll take AI from proof of concept to production, navigate the chaos of real customer environments, and make complex agentic systems actually deliver business outcomes. This is a consulting and hands-on delivery role. You will write production-grade code, architect intelligent agent workflows, debug pipelines at midnight before a go-live, and then walk into a boardroom the next morning to explain what you built and why it matters. You'll own the full arc — from discovery and design through deployment and adoption — and your success is measured by one thing: does the customer's business actually change because of the AI you delivered? If you love AI deeply, want to get your hands dirty across the entire delivery lifecycle, and feel equally at home talking to a team and writing LLM orchestration logic, this role was written for you. What You'll Do Enterprise AI Delivery Embed with enterprise customers to understand their workflows, data environments, and operational constraints — and build AI solutions that fit their reality, not a sanitized sandbox Own implementations end-to-end: from scoping and solution design through integration, testing, deployment, and handoff Rapidly diagnose technical blockers — messy data, broken integrations, edge cases, legacy system quirks — and solve them yourself without waiting on a queue Agentic AI Engineering Design, build, and orchestrate multi-step AI agents that automate complex workflows across enterprise systems Work with frameworks like LangGraph, LangChain, or similar to architect reliable, production-ready agentic pipelines Apply sound judgment about what should be automated vs. what requires human-in-the-loop, and design accordingly Continuously tune agent behavior based on real-world usage patterns you observe in the field Technical Breadth Build custom integrations, connectors, and data pipelines to bridge enterprise tech stacks with AI infrastructure Work across the stack — APIs, vector databases, LLM APIs, cloud infrastructure, and front-end surfaces — to deliver complete, working systems Write clean, maintainable code that others can build on top of Product & Feedback Loop Identify patterns across customer engagements that signal genuine product opportunities — and advocate for them with engineering and product teams with precision Contribute to building internal tooling and repeatable delivery assets (deployment templates, agent blueprints, evaluation frameworks) that make the next implementation faster Optionally: take ownership of building product features or internal tools that emerge from your field insights