The Applied AI team at Redis - pioneers in AI-powered development tools. (Also, our only attendees.)
Applied AI Engineer & Redis OM Creator
Andrew is the creator of Redis OM for Python and an experienced Python developer. When not building APIs or running D&D campaigns, he's probably rate-limited on Claude Code (and definitely banned for life). He'll share possibly controversial opinions about the future of software development.
Manager, Applied AI Engineering
Tyler leads the Applied AI Engineering team at Redis, where he's become a prominent voice in the vector database and semantic search community. His background spans from traditional software engineering to cutting-edge AI infrastructure, making him uniquely positioned to understand what developers actually need (hint: it's more than just vector databases). Tyler's work on multimodal RAG systems and his advocacy for practical AI implementations have made him a sought-after speaker in the AI engineering space.
Principal Applied AI Engineer
Brian is a Principal Applied AI Engineer and the mastermind behind Redis OM Spring, bringing his deep Java expertise to the AI world. As an author, instructor, and avid open-source contributor, he's been bridging enterprise Java development with cutting-edge AI capabilities for years. His work on Spring AI and LangGraph integrations has made him a go-to expert for organizations looking to add AI features to their production Java applications. Brian's talks are known for their practical, no-nonsense approach to enterprise AI. But he'd rather be coding in Rust than talking to you.
Applied AI Engineer & Optimization Specialist
Robert brings a unique research background to applied AI engineering, with expertise spanning from physics to machine learning optimization. His work on Bayesian optimization and semantic caching represents the perfect blend of theoretical rigor and practical application. Robert's research-driven approach to AI engineering focuses on making AI systems not just work, but work efficiently and cost-effectively. His contributions to retrieval optimization have helped organizations slash their AI infrastructure costs while boosting performance.
Senior Applied AI Engineer
Justin is a machine learning engineer with a passion for recommender systems and practical AI applications. His expertise in collaborative filtering and content-based filtering systems demonstrates that there's a whole world of AI beyond just language models. As a key contributor to Redis Vector Library (RedisVL), Justin has simplified AI application development for thousands of developers. When not bridging the gap between academic ML research and production systems, you can find him on the rugby pitch—because tackling complex algorithms and tackling opponents both require strategic thinking and fearless execution.
Applied AI Engineer
Han brings deep technical expertise in AI system architecture and scalable engineering solutions to the Applied AI team. With a strong background in software engineering and distributed systems, he focuses on building robust AI infrastructure that enables seamless integration of AI capabilities into production applications. His work bridges the gap between cutting-edge AI research and practical, enterprise-ready implementations.
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