DL
Knowledge Graph
Diverse Lynx India
Location not stated
1 month ago
- RAG
- AI
- Python
- Vector Search
- LangGraph
- LangChain
- LLM APIs
- OpenAI
- Bedrock
- Azure OpenAI
- Neptune
- Neo4j
- Pinecone
- FAISS
1 month ago
| Responsibilities |
| Design and build ontology-driven knowledge graphs for domains, requirements, code assets, design artifacts, and enterprise knowledge sources. |
| Model entities, relationships, taxonomies, hierarchies, inheritance rules, and metadata needed for agent reasoning and retrieval. |
| Implement graph-based retrieval, hybrid RAG, semantic search, and context-ranking patterns to improve grounding and explainability. |
| Integrate graph databases with agent workflows, prompt templates, and evaluation datasets. |
| Validate knowledge quality, graph consistency, relationship accuracy, lineage, and provenance of retrieved context. |
| Collaborate with domain, backend, and agent teams to operationalize reusable knowledge models and graph-backed guardrails. |
| Qualifications |
| Bachelor's or Master's degree in Computer Science, AI, Data Engineering, or a related discipline. |
| 6+ years of total experience, including 2+ years in agent development, knowledge graphs, ontology modelling, or semantic retrieval. |
| Strong Python programming expertise. |
| Hands-on experience with graph databases, graph query languages, ontology design, embeddings, vector search, and RAG patterns. |
| Experience integrating knowledge graphs with LLM applications, agent workflows, and enterprise knowledge sources. |
| Familiarity with evaluation frameworks, grounding-quality checks, data lineage, provenance, and AI guardrails. |
| Mandatory Tech Stack |
| Language: Python. |
| Agent Frameworks: LangGraph / LangChain or equivalent agent-orchestration frameworks. |
| LLM APIs: OpenAI / Anthropic / Bedrock / Azure OpenAI or equivalent foundation-model APIs. |
| Graph DB: Amazon Neptune / Neo4j and query languages such as Cypher, Gremlin, or SPARQL. |
| Knowledge Modeling: Ontology design, taxonomies, hierarchical inheritance, entity / relationship modelling, metadata, lineage, and provenance. |
| Retrieval: Vector databases such as Pinecone / FAISS, embeddings, semantic search, hybrid RAG, and grounding-quality checks. |
Knowledge Graph · Diverse Lynx India