Job Description

Job Title: Senior Data Scientist – GenAI & Knowledge Graphs

Location: Seattle, WA, US

Department: AI & Data Science

Employment Type: Full-time

Job Summary

We are looking for a Senior Data Scientist with hands-on expertise in building Generative AI applications using Knowledge Graphs, Graph Databases, and multi-agent systems. The ideal candidate will have strong experience in LLM-driven development, agentic AI workflows, and semantic data modeling using OWL and RDF.

Key Responsibilities

• Build and maintain graph-based data pipelines using technologies like Neo4j, Amazon Neptune, or Stardog.
• Design and implement knowledge graphs and ontologies using OWL, Protégé, TopBraid, or similar tools.
• Integrate knowledge graphs with GenAI pipelines, improving context grounding and retrieval for LLM-based applications.
• Develop and orchestrate multi-agent systems using LangGraph, CrewAI, or AutoGen, including agent-to-agent (A2A) communication, memory modules, and reasoning chains.
• Leverage MCP servers and agent runtime engines to deploy agent-based GenAI applications for real-world scenarios such as customer support, content synthesis, and document analysis.
• Work on RAG architectures involving embedding models, vector stores (e.g., FAISS, Pinecone), and structured semantic layers.
• Collaborate with engineers and junior data scientists on project delivery and model deployment.
• Write clean, reusable code in Python using ML/LLM frameworks such as LangChain, HuggingFace, and OpenAI SDKs.

Required Skills & Experience

• 5–8 years of experience in data science, with 2+ years in LLM/GenAI development.
• Hands-on experience with GraphDBs (Neo4j, Neptune, TigerGraph) and SPARQL or Cypher.
• Proficiency in ontology modeling using OWL/RDF and familiarity with semantic reasoning tools.
• Strong experience in building agentic AI applications, including use of LangGraph, AutoGen, or CrewAI.
• Understanding of multi-agent communication protocols (A2A), agent memory, and orchestration layers.
• Solid understanding of data wrangling, NLP, and embedding models (sentence-transformers, OpenAI embeddings, etc.).
• Python proficiency and experience with REST APIs, data processing libraries (pandas, NumPy), and JSON-LD.

Preferred Qualifications

• Master’s degree in Data Science, Computer Science, AI/ML, or a related field.
• Knowledge of cloud-native deployment is a plus (though not mandatory).
• Open-source contributions, blog posts, or internal project showcases in the GenAI/Knowledge Graph space.

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