Full Stack Developer

We are seeking an experienced Senior Graph Developer to design, build, and scale enterprise-level graph database solutions. The ideal candidate will have extensive hands-on experience with Amazon Neptune, SPARQL/Gremlin, data engineering pipelines, and GraphRAG architectures. This role will focus on developing knowledge graphs, optimizing graph database environments, and integrating graph technologies with Generative AI solutions to improve contextual understanding and reduce AI hallucinations.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Information Architecture, or a related technical field.
• 10+ years of professional experience in software engineering, data engineering, or graph technology development.
• Extensive hands-on experience designing, implementing, and managing production-scale Amazon Neptune graph database environments.
• Strong expertise in graph query languages such as SPARQL and/or Gremlin.
• Strong experience designing and developing data engineering and ETL pipelines.
• Practical experience implementing GraphRAG solutions and integrating knowledge graphs with AI/ML workflows.
• Advanced programming experience in Python or JVM-based languages such as Java or Scala.
• Strong understanding of AWS cloud services, including:
  • Amazon Neptune
  • AWS S3
  • Amazon RDS
  • Amazon ECS
• Experience designing enterprise ontologies, taxonomies, and graph schemas.

Preferred Qualifications

• Experience with Generative AI integration and LLM-based applications.
• Experience building backend APIs and services.
• Experience integrating graph databases with vector databases and hybrid search architectures.
• Strong understanding of knowledge graph modeling and semantic technologies.
• Experience working with large-scale distributed systems.

Key Responsibilities

• Design and develop enterprise ontologies, taxonomies, and graph schemas representing complex business domains.
• Architect, configure, monitor, and scale Amazon Neptune environments capable of handling billions of relationships.
• Develop and optimize graph data models, queries, and performance tuning strategies using SPARQL and/or Gremlin.
• Build and maintain data ingestion frameworks and ETL pipelines for graph-based applications.
• Integrate knowledge graphs into GraphRAG pipelines to improve AI response accuracy, contextual grounding, and reduce hallucinations.
• Partner with AI/ML teams to implement hybrid search solutions combining graph traversal capabilities with vector database technologies.
• Develop scalable backend services and APIs supporting graph-powered applications.
• Collaborate with architecture, engineering, and business teams to translate complex requirements into scalable graph solutions.
• Ensure reliability, security, and performance of graph database infrastructure.