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Orchestrator Pod #1 - Agent Developer with Knowledge Graph specialization

Agent Developer with Knowledge Graph specialization

AI + Python+Agentic Framework

Agent Developer with Knowledge Graph specialization

Specialist agent developer focused on knowledge graph design, ontology modelling, graph-based retrieval, and grounding AI agent outputs with structured enterprise knowledge.

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.