About the job
We're building a new AI Enablement team to create the next generation of agent-powered tools that ground AI in real operational workflows. Our goal: help internal teams demystify their own workflows, then deploy agentic systems that reason over data, take action, and deliver measurable outcomes. We don't build in a vacuum. You'll use our own platform to solve real business problems internally—then selectively commercialize that same stack for customers. This is a 0→1 team. We're looking for a sharp, product-minded engineer who thrives in ambiguity, moves fast, and loves building systems from scratch alongside customers and cross-functional partners. You'll work closely with product, forward-deployed engineers, data scientists, and applied AI teams to turn real-world problems into scalable production solutions.
Responsibilities
Own full-stack features and projects end-to-end — from design through production deployment — within a larger product area
Develop reliable backend services in Typescript/Python, work with distributed systems, data pipelines, and AI/ML infrastructure
Integrate LLMs, vector databases, and agentic frameworks to power intelligent workflows
Ship quickly through tight experimentation loops while maintaining high quality and reliability
Adapt across the stack and learn new tools as needed to solve real problems end-to-end
Qualifications
Minimum
3+ years of full-time software engineering experience
Solid full-stack fundamentals with production ownership of features you've shipped
Familiarity with LLMs, embeddings, vector databases, or modern AI data products/tools
Exposure to distributed systems and cloud-based architectures
Preferred
Strong product intuition and customer empathy
Entrepreneurial mindset, building and implementing agentic workflows
Ownership mentality — you see problems through to outcomes
Comfort collaborating across engineering, product, data science, and applied AI
Excitement about building agentic systems that make AI genuinely useful in the real world