Staff AI Software Engineer - Platform

THE POSITION Our roster has an opening with your name on it As the senior-most technical Knowledge & Context Engineer, you will design and operationalized a centralized multimodal memory architecture spanning vector retrieval, knowledge graphs, ontologies, metadata systems, runtime memory injection, and lineage and governance frameworks. This role sits at the intersection of AI engineering, distributed systems, and applied intelligence. You will lead the strategy and execution for how enterprise knowledge is structured, represented, governed, retrieved, and operationalized across agents, copilots, automation systems, and customer-facing AI products. Importantly, you will sit at the cutting edge of building the cognitive substrate for an AI-native enterprise. Come join us as a hands-on thought leader who innovates by doing. In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs. THE GAME PLAN Everyone on our team has a part to play Build foundational content intelligence systems: Design systems for ingestion, indexing, embeddings, metadata, retrieval, lineage, governance, and auditability that can support both internal and customer-facing AI use cases. This includes customers, media and marketing assets, product features, production code, websites, game sounds, customer service experiences, operational workflows, and other enterprise content. Establish enterprise knowledge graphs and ontologies: Define and implement a regulatory-compliant knowledge graph strategy that creates deep context about FanDuel’s products, employees, operations, customers, and systems. Own the design of graph databases, semantic models, ontologies, entity resolution patterns, and relationships across vector and non-vector data. Build the connective tissue that allows AI systems and agents to reason over enterprise context with precision, transparency, and control. Design secure memory patterns for agents: Create reusable design patterns for how AI agents acquire, store, retrieve, update, and discard context during runtime. This includes short-term memory, long-term memory, episodic memory, summarization, context injection, retrieval augmentation, and governed memory sharing across tools and systems. Ensure memory systems are efficient, secure, auditable, and appropriate for regulated environments. Champion responsible AI development: Ensure knowledge, retrieval, graph, and memory systems meet regulatory requirements, ethical standards, privacy obligations, and responsible gaming principles. Build safeguards, access controls, provenance, explainability, monitoring, and auditability into the platform from day one. In six months, you’ll know you’re heading on the right path if you’ve built: Reusable runtime memory patterns that are being used by multiple agents to securely acquire, retrieve, summarize, and apply context during execution. A production-grade graph and ontology framework connecting products, customers, employees, operations, systems, and content with clear lineage, access controls, and regulatory compliance. The first context sets of centralized, governed multimodal vector store and retrieval layer supporting AI applications across customer, product, marketing, engineering, operations, and support domains. Teams can build AI solutions faster because retrieval, memory, metadata, governance, and knowledge graph capabilities are available as shared primitives rather than bespoke pipelines. THE STATS What we're looking for in our next teammate 7+ years of engineering experience, preferably working with large distributed systems that support a mix of data and software development activities. Bonus to consulting engineers and people with experience on early start up teams Track record of taking products from concept to launch in fast-moving, ambiguous environments. Pract