GenAI Engineer - Database at NielsenIQ

  Role Summary We are seeking a Lead, GenAI Database Engineer to design, build, and scale next-generation AI-native data platforms that power Generative AI, Retrieval-Augmented Generation (RAG), and Knowledge Graph–driven applications. This role sits at the intersection of graph theory, vector search, relational data modeling, and AI systems, with a strong emphasis on hands-on technical leadership. You will be responsible for architecting and implementing multi-modal data ecosystems that combine Graph Databases, Vector Databases, and Relational Databases to enable explainable, scalable, and production-grade GenAI solutions.   Key Responsibilities 1. GenAI Data Architecture & Platform Design • Design and lead end-to-end GenAI data architectures supporting: • Retrieval-Augmented Generation (RAG) • Knowledge Graph–augmented LLMs (good to have) • Hybrid semantic + symbolic reasoning systems • Architect polyglot persistence strategies, determining where Graph, Vector, and Relational databases are used and how they interoperate. • Establish data standards, schemas, indexing strategies, and performance benchmarks for AI-driven workloads.2. Graph Database & Knowledge Graph (Hands-on) • Own the design, development, and optimization of Knowledge Graphs for enterprise-scale use cases. • Model complex domains using nodes, edges, properties, and ontologies. • Implement advanced graph capabilities: • Entity resolution and linking • Schema and ontology design (RDF / OWL where applicable) • Graph inference, traversal, and reasoning • Hands-on experience with graph query languages. • Engineer performant graph pipelines using databases.  3. Vector Databases & Semantic Retrieval • Lead the implementation of vector-based retrieval systems to support semantic search and RAG pipelines. • Design and manage: • Embedding storage and lifecycle management • Chunking, indexing, and hybrid retrieval strategies • Optimize vector similarity search for scale, latency, and relevance.  4. Relational Database & Enterprise Data Engineering • Design and maintain high-performance relational schemas supporting transactional, analytical, and AI workloads. • Leverage RDBMS platforms such as: • PostgreSQL • MySQL • SQL Server • Oracle • Implement: • Advanced indexing strategies • Query optimization • Stored procedures and data integrity constraints • Ensure smooth integration between relational systems and Graph/Vector layers.  5. Technical Leadership & Governance • Act as a technical lead and mentor for database and GenAI engineers. • Review architecture designs, code, and data models. • Establish best practices for: • AI data governance • Security, privacy, and compliance • Metadata management and lineage • Partner with product, ML, and business stakeholders to translate use cases into scalable data solutions.  Required Qualifications Core Technical Skills • Expert-level hands-on experience with: • Graph Databases • Vector Databases • Relational Databases • Good to have experience in designing and implementing Knowledge Graphs in production. • Deep understanding of: • Graph theory and graph algorithms • Vector similarity search and embeddings • SQL and relational modeling Leadership Competencies • Strong system-level thinking across data, AI, and infrastructure. • Ability to influence architecture decisions across teams. • Excellent communication skills with both technical and non-technical stakeholders. • Bias toward ownership, hands-on execution, and continuous improvement.