Job Description

Job Title: Data Scientist – GenAI & Knowledge Graphs

Location: Seattle, WA, US

Employment Type: Full-time

Job Summary

We are hiring a Data Scientist to support the development of Generative AI applications that leverage knowledge graphs, GraphDBs, and multi-agent orchestration. This role is hands-on, focused on practical implementation of GenAI patterns using state-of-the-art open-source libraries and graph technologies.

Key Responsibilities

• Develop graph data models using Neo4j, RDF, and SPARQL/Cypher for building semantic knowledge representations.
• Support the design and enrichment of ontologies using OWL and Protégé, aligning data schemas for GenAI use cases.
• Build components of agentic AI systems using frameworks like LangGraph, CrewAI, or AutoGen under senior guidance.
• Assist in implementing RAG pipelines, working with vector databases and embedding models for improved document search.
• Implement agent-to-agent (A2A) interaction flows and agent memory structures in prototype-level applications.
• Work with Python, integrating APIs from LLM providers (OpenAI, Anthropic, etc.) with knowledge graph backends.
• Maintain documentation, test cases, and reusable code modules for internal projects.

Required Skills & Experience

• 2–4 years of experience in data science or NLP/ML roles.
• Exposure to GraphDBs like Neo4j or Neptune and basic query languages like Cypher or SPARQL.
• Understanding of ontology basics, OWL standards, and tools like Protégé.
• Familiarity with GenAI tools and libraries (LangChain, LangGraph, CrewAI, etc.).
• Strong Python skills with ability to work with REST APIs, LLM SDKs, and embedding models.
• Good documentation, debugging, and collaboration skills.

Preferred Qualifications

• Bachelor’s degree in Data Science, Computer Science, or related field.
• Familiarity with agent protocols (MCP, A2A) and experience participating in GenAI POCs or hackathons.
• Interest in growing expertise in semantic AI, graph-based ML, and agentic systems.

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