Forward Deployed Engineer - AI Solutions (Gracenote Division)

This opportunity is for Nielsen’s Gracenote division - the premier entertainment metadata company in the world, powering the search, discovery, and navigation systems for the globe's biggest tech, automotive, and media giants. As entertainment becomes more fragmented and AI reshapes the way people discover and engage with content, trusted data and technology are powering the future and are the foundation for better experiences. In partnership with our clients and by fully embracing AI, we are reinventing the business, products and teams to capture a once-in-a-lifetime opportunity which will significantly impact the media and entertainment ecosystem as a whole. We are seeking a highly technical, customer-focused Product Solutions Architect (PSA) to lead technical pre-sales and implementation for our suite of API and AI products, with a primary focus on creating agents that solve customer problems with our newly launched and forthcoming Gracenote Model Context Protocol (MCP) Servers. You will serve as the technical bridge between our customers and our product/engineering teams, enabling B2B clients to successfully integrate our authoritative metadata into their LLM-powered discovery experiences, and highlight the value of AI-first approaches to their business problems. Key Responsibilities Technical Pre-Sales & Consultation: Partner with Sales to apply Gracenote’s GTM strategy, demonstrate the value of AI solutions to a variety of customer stakeholders (Product, Technology, or Marketing -oriented profiles). Lead discovery sessions, understand customer use-cases, AI -based architecture and identify tangible value propositions for how Gracenote metadata can improve customer’s discovery experience using AI. Elaborate and run proof of concepts. Own and secure customer engagement and adoption through contract signature. Develop Agentic Solutions: Design, build, and maintain a portfolio of focused, single-purpose AI agents that demonstrate specific technical capabilities of the Gracenote Video MCP Server — translating complex, multi-step entertainment workflows (such as conversational search, catalog enrichment, and personalized recommendations) into tangible, hands-on prototypes Deploy on Frameworks: Possess experience deploying these agents on various agentic frameworks, such as those provided by Google or AWS, to prove their efficacy Solution Architecture: Analyze complex, siloed customer environments to recommend optimal integration patterns and map Gracenote tools/resources to customer-specific AI -based workflows. Act as "Customer Zero" to familiarize with, test and validate new MCP tools and features before they roll out to clients. Implementation Excellence: Guide developers through the technical adoption process, providing code-level guidance, documenting integration use cases, and helping customers leverage JSON-RPC 2.0 standards for seamless API/server interaction. Product Advocacy: Function as the "voice of the customer" to influence product roadmaps, translating customer requirements into actionable product inquiries (PIs) and technical documentation. Strategic Engagement: Establish standardized SLAs and best practices for implementation, ensuring consistent delivery across Tier 1 and Tier 2 global accounts. Technical Mastery: Solid grasp of agent creation and agentic deployment frameworks and MCP tooling, specifically how LLMs utilize MCP primitives (Tools, Resources, Prompts), JSON-RPC 2.0, and AWS ecosystem with specific, practical experience leveraging AWS Cognito for identity management, user authentication, and secure API access control. Agentic AI & Orchestration: Experience designing, deploying, or observing multi-step AI agents using one or more of the following frameworks: Google ADK, AWS Bedrock Agents, LangChain, or LlamaIndex preferred. Deep understanding of tool chaining (MCP), system prompt engineering, function calling across major LLMs (Gemini, Claude, GPT), and step-level tracing or response evaluation. Consultative Expertise: Proven ability to navigate complex enterprise security (OAuth/OIDC) and data privacy requirements while effectively communicating technical solutions to diverse audiences, from C-level executives to engineering teams. Experience: Strong experience in Python or TypeScript/Node.js to support PoCs and integration with B2B enterprise APIs. Experience connecting MCP clients and servers to managed enterprise foundation models via AWS Bedrock, Google Cloud Vertex AI, Microsoft Azure AI / Foundry, etc. Familiarity with large-scale structured datasets, content discovery architectures, and search & recommendations engine mechanics. Familiarity with the CTV/Streaming and the entertainment ecosystem is extremely welcome but not required. Customer interaction skills: Proven ability to bridge the gap between complex LLM/MCP architectures and business objectives, leveraging strong consultative leadership and stakeholder management skills to guide discovery, execute Proof of C…