Eragon is an enterprise AI startup building an agentic AI operating system that exposes business software through a natural-language LLM interface.
JOB DESCRIPTION We’re looking for an AI Product Manager to define, build, and scale AI-powered products. In this role, you’ll sit at the intersection of product, engineering, and go-to-market—translating customer needs into AI-driven solutions and driving them from concept to production. You’ll work closely with applied AI and engineering teams to shape product direction, prioritize features, and ensure we’re delivering real value to customers. KEY RESPONSIBILITIES - Product Strategy: Define product vision, roadmap, and priorities for AI-powered features and systems - Customer Insight: Work directly with customers to understand workflows, pain points, and opportunities for AI-driven automation - Cross-Functional Execution: Partner with engineering, research, and design to ship high-impact products - AI Product Definition: Translate ambiguous AI capabilities into clear product requirements, specs, and user experiences - Iteration & Feedback Loops: Use data, usage, and customer feedback to continuously improve product performance - Go-to-Market Collaboration: Work with sales and marketing to position and launch products effectively MINIMUM QUALIFICATIONS - Experience: 3–7+ years in product management, preferably with AI/ML-powered products - Technical Fluency: Strong understanding of AI/ML systems (e.g., LLMs, agents, data pipelines), without needing to build models yourself - Execution Track Record: Proven ability to take products from concept to launch in fast-paced environments - Customer Orientation: Experience working closely with users and translating needs into product decisions - Communication: Strong ability to align technical and non-technical stakeholders NICE TO HAVE (OPTIONAL BUT VALUABLE) - Experience building or working with LLM-based products, agents, or workflow automation tools - Background in engineering, data science, or technical product roles - Experience in early-stage startups