GenAI Engineer - Database
NielsenIQ View all jobs
- Pune, Maharashtra
- Permanent
- Full-time
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- Flexible working environment
- Volunteer time off
- LinkedIn Learning
- Employee-Assistance-Program (EAP)