The role involves designing, building, and scaling AI-native data platforms to support Generative AI, Retrieval-Augmented Generation, and Knowledge Graph applications. The engineer will architect multi-modal data ecosystems that integrate graph, vector, and relational databases to ensure scalable and production-grade solutions. Responsibilities include establishing data standards, schemas, and performance benchmarks for AI-driven workloads. The position requires hands-on development of knowledge graphs and vector-based retrieval systems to optimize semantic search and reasoning. Additionally, the role entails providing technical leadership, mentoring team members, and ensuring data governance and security across complex data architectures.

Candidates must possess at least five years of overall experience in advanced data platforms, including three years specifically in GenAI or graph database-driven systems. Required technical expertise includes hands-on proficiency with graph, vector, and relational databases, as well as a deep understanding of graph theory, vector similarity search, and SQL. The role requires strong system-level thinking, the ability to influence architectural decisions, and excellent communication skills. No specific degree or professional qualification is explicitly mentioned as a requirement.