GenAI Engineer - Database

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

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Weekend work

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About this role

GenAI Engineer - Database Pune, MH, India Company Description: Job Description: 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

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