Job Opportunities API

Job Description We are seeking a technical, customer-focused Product Solutions Architect to lead customer technical pre-sales and implementation for our suite of metadata products and AI products, including the Gracenote Model Context Protocol (MCP) Servers. You will serve as the primary technical subject matter expert for the Gracenote suite of products, enabling B2B clients to successfully integrate our authoritative metadata into their discovery experience including AI-powered integrations. You will lead the end-to-end customer technical engagement lifecycle of technical solutions, from pre-sales consultation, requirements gathering, product solutioning and proof-of-concept development to post-implementation support and strategic product advocacy. Key Responsibilities • Technical Pre-Sales & Strategy: Partner with Sales to lead technical discovery sessions, assessing client needs and demonstrating the value of Gracenote’s solutions. Blending standard solution selling with AI/MCP value propositions, you will identify tangible ways Gracenote products can improve discovery experiences. Lead technical workshops, PoCs and own customer integration planning through adoption and production deployment.. • Implementation & Delivery: Act as the technical anchor during onboarding, guiding developers through integration of Gracenote metadata available via standard APIs and Agentic frameworks. Provide code-level guidance, document integration use cases, and ensure best practices for deployment on frameworks like Google or AWS. • Operational & Strategic Advocacy: Function as the "voice of the customer" to influence product roadmaps. Translate customer requirements into actionable requests to the product team. Update or create technical documentation to drive customer self-sufficiency. Serve as "Customer Zero" to test and validate new products before rollout. • Technical Development: Design, build, and maintain focused, single-purpose AI agents that demonstrate technical capabilities of the Gracenote MCP Server. Support PoCs involving standard API/XML data integration alongside modern AI-agentic engineering (function calling, token optimization, and context management).