About the role
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.
Requirements:
- 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.