Description

You build and productionize Vertex AI-based Retrieval Augmented Generation (RAG) systems and design reliable tool-using agents.

This role is hybrid.

Responsibilities

• Design and implement end-to-end RAG pipelines on Google Cloud / Vertex AI, covering chunking, embeddings, indexing, retrieval, and reranking.
• Build agentic workflows, implementing tool use, planning, reflection/guardrails, and structured outputs using Python-first frameworks.
• Integrate agents with Vector Databases (e.g., Vertex Vector Search, Pinecone) and Graph Databases (e.g., Neo4j).
• Create data ingestion pipelines from various sources and define system evaluation metrics, including grounding and hallucination checks.
• Ship features to production, focusing on APIs, observability, cost optimization, and CI/CD practices.

Required Skills

• 10+ years of professional experience in software development.
• Expertise in strong Python, including clean architecture, async programming, typing, and testing.
• Proven experience building production-grade RAG solutions involving hybrid search and chunking strategies.
• Hands-on experience with Vertex AI and core GCP services (IAM, Cloud Run/GKE).
• Proficiency with at least one agentic framework such as LangGraph, LangChain, LlamaIndex, or AutoGen.
• Solid knowledge of vector search concepts and production experience with a vector database.
• Familiarity with graph data modeling and basic querying languages (e.g., Cypher).
• Strong engineering discipline: code reviews, telemetry implementation, and secure-by-design practices.

Key Skills

Education

Any Graduate