Principal AI Software Engineer

Role descriptionJOB DESCRIPTIONAgentic AI EngineerJob TitleAgentic AI Engineer (Python) — Vertex AI RAG + Graph/Vector DatastoresRole summaryWe’re looking for a strong agentic AI developer who can build and productionize Vertex AI–based RAG systems (Vertex AI Search / Vertex AI RAG patterns), design reliable tool-using agents, and work comfortably with vector databases and graph databases. You’ll own end-to-end delivery: ingestion → retrieval → agent orchestration → evaluation → deployment.What you’ll doDesign and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.Integrate agents with Graph DBs (e.g., Neo4j, JanusGraph, Neptune) and Vector DBs (e.g., Vertex Vector Search, Pinecone, Weaviate, Milvus, pgvector).Create robust data ingestion/ETL from PDFs, docs, webpages, and internal sources; implement metadata strategy and access control.Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.Must-have skillsStrong Python (clean architecture, async, testing, typing, packaging).Proven experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).Hands-on with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).Experience with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.Solid knowledge of vector search concepts and at least one vector DB in production.Comfortable with graph data modeling and graph querying (Cypher/Gremlin/SPARQL basics).Strong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset.Nice-to-haveKnowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).Other detailsSalary range $ 60000 to 140000 $