GenAI Engineer - Database

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.