{"schemaVersion":"jobsearcher.job.v1","id":"6697f810876e8c57c394109b","url":"https://jobsearcher.com/jobs/6697f810876e8c57c394109b","canonicalUrl":"https://jobsearcher.com/jobs/6697f810876e8c57c394109b","title":"Agentic AI Lead (Python)","description":"Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).\r\nBuild agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.\r\nIntegrate agents with Graph DBs (e.g., Neo4j, JanusGraph, Neptune) and Vector DBs (e.g., Vertex Vector Search, Pinecone, Weaviate, Milvus, pgvector).\r\nCreate robust data ingestion/ETL from PDFs, docs, webpages, and internal sources; implement metadata strategy and access control.\r\nDefine and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.\r\nShip to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.\r\nMust-have skills Strong Python (clean architecture, async, testing, typing, packaging).\r\nProven experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).\r\nHands-on with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).\r\nExperience with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.\r\nSolid knowledge of vector search concepts and at least one vector DB in production.\r\nComfortable with graph data modeling and graph querying (Cypher/Gremlin/SPARQL basics).\r\nStrong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset.\r\nJ-18808-Ljbffr","company":"Cloudious","rawCompany":"cloudious","city":"Berkeley Heights","state":"NJ","isRemote":false,"isActive":false,"createdAt":"2026-07-16T01:36:13.833Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Agentic AI Lead (Python)","description":"Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).\r\nBuild agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.\r\nIntegrate agents with Graph DBs (e.g., Neo4j, JanusGraph, Neptune) and Vector DBs (e.g., Vertex Vector Search, Pinecone, Weaviate, Milvus, pgvector).\r\nCreate robust data ingestion/ETL from PDFs, docs, webpages, and internal sources; implement metadata strategy and access control.\r\nDefine and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.\r\nShip to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.\r\nMust-have skills Strong Python (clean architecture, async, testing, typing, packaging).\r\nProven experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).\r\nHands-on with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).\r\nExperience with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.\r\nSolid knowledge of vector search concepts and at least one vector DB in production.\r\nComfortable with graph data modeling and graph querying (Cypher/Gremlin/SPARQL basics).\r\nStrong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset.\r\nJ-18808-Ljbffr","datePosted":"2026-07-16T01:36:13.833Z","dateModified":"2026-07-16T01:36:13.833Z","hiringOrganization":{"@type":"Organization","name":"Cloudious","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Berkeley Heights","addressRegion":"NJ","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"6697f810876e8c57c394109b"},"url":"https://jobsearcher.com/jobs/6697f810876e8c57c394109b"}}