jobs in the wild.

📍 Remote | 🕒 5–8+ yrs | 💼 Full-time We’re looking for a hands-on engineer with deep graph database expertise to architect and optimize large-scale knowledge graph systems powering enterprise GenAI applications. What you’ll do: ▪ Build scalable knowledge graphs: entity extraction, relationship inference, entity linking, semantic enrichment, at billions of nodes ▪ Design graph-backed RAG systems and multi-hop reasoning ▪ Optimize Neo4j performance: Cypher, query planning, b-tree/full-text/HNSW indexing, traversal and caching ▪ Build distributed ingest pipelines with idempotence, state management, consistency and monitoring ▪ Evaluate and integrate graph DBs, vector stores, semantic search and LLM APIs What you bring: ‣ Neo4j, graph data modeling, and graph algorithms (PageRank, centrality, community detection, k-NN, link prediction) ‣ Data engineering: ETL/ELT, data lineage and quality, structured and unstructured data at scale ‣ SQL, NoSQL, and vector databases (ANN, HNSW) ‣ Azure: Functions, AKS, CosmosDB, Durable Functions, Service Bus, Event Hub, Event Grid, Synapse, Data Factory, Cognitive Search, Key Vault, RBAC ‣ IaC (Bicep, Terraform, ARM), distributed systems, event-driven and async architectures, microservices ‣ Python/Java, API design, observability (App Insights, ELK, Prometheus) ‣ GenAI: OpenAI, Azure OpenAI, Claude, prompt engineering, RAG, embeddings, inference and cost optimization ‣ 2+ yrs building Neo4j knowledge graphs and 2+ yrs deploying GenAI systems ‣ Master’s in CS, Engineering or Math (Ph.D. preferred) Nice to have: › Neo4j or Azure certifications (AZ-104, AZ-305, DP-900) › GNNs (PyTorch Geometric, DGL, GraphSAGE, Node2Vec), RDF/semantic web › Spark GraphX, Gremlin, sharding, and multi-tenancy at 100M+ nodes › Docker, Kubernetes, Helm › Open-source contributions 📩 Apply now or DM me | Refer someone great! 👇 #Hiring #KnowledgeGraph #Neo4j #GraphDatabase #GenAI #RAG