Job Summary:
We are seeking a senior Agentic AI Developer / Lead with 10+ years of experience to design, build, and productionize agentic AI and Retrieval-Augmented Generation (RAG) solutions using Google Cloud and Vertex AI. The role will own end-to-end delivery across data ingestion, retrieval, agent orchestration, evaluation, and production deployment. The ideal candidate will have strong Python engineering skills, hands-on experience with Vertex AI and GCP, agentic frameworks, vector databases, graph databases, and production-grade AI systems.
Key Responsibilities:
• Design and implement RAG pipelines on Google Cloud and Vertex AI, including chunking, embeddings, indexing, retrieval, reranking, and grounding.
• Build agentic workflows incorporating tool use, planning, reflection, guardrails, structured outputs, and tool/function calling patterns.
• Integrate AI agents with graph databases such as Neo4j, JanusGraph, and Neptune.
• Integrate AI solutions with vector databases such as Vertex Vector Search, Pinecone, Weaviate, Milvus, and pgvector.
• Develop robust data ingestion and ETL pipelines for PDFs, documents, webpages, and internal data sources.
• Implement metadata strategies and access control for AI and data pipelines.
• Define and execute evaluation strategies covering retrieval metrics, answer quality, hallucination detection, and grounding checks.
• Continuously evaluate and improve RAG and agentic AI system quality.
• Develop production-ready APIs and deploy AI solutions using appropriate cloud infrastructure.
• Implement monitoring, observability, security, CI/CD, and reliability practices for production AI systems.
• Optimize AI applications for cost, performance, scalability, and reliability.
• Apply strong software engineering practices, including code reviews, testing, telemetry, and secure-by-design development.
Required Qualifications:
• 10+ years of professional technology experience with senior-level experience in AI, software engineering, data engineering, or related disciplines.
• Strong hands-on experience with Python, including clean architecture, asynchronous programming, testing, typing, and packaging.
• Proven experience building and productionizing RAG solutions.
• Strong understanding of hybrid search, reranking, chunking strategies, embeddings, prompt design, and schema design.
• Hands-on experience with Vertex AI and Google Cloud Platform fundamentals, including IAM, logging, monitoring, Cloud Run, GKE, and cloud storage.
• Experience with at least one agentic AI framework, such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, or AutoGen.
• Experience implementing tool use and function calling patterns for AI agents.
• Solid understanding of vector search concepts and hands-on production experience with at least one vector database.
• Experience with graph data modeling and graph querying, including familiarity with Cypher, Gremlin, or SPARQL.
• Strong software engineering practices, including code reviews, testing, telemetry, security, and reliability engineering.
Preferred Qualifications:
• Experience using knowledge graphs for RAG, including entity linking, graph traversal, and retrieval fusion.
• Experience with streaming and messaging technologies such as Pub/Sub or Kafka.
• Experience with document processing pipelines and Document AI.
• Experience with multilingual retrieval.
• Experience with evaluation tools such as RAGAS, TruLens, or custom evaluation harnesses.
• Experience with prompt and version management.
• Experience with frontend integration using React or Next.js.
• Experience developing internal developer tooling or platform enablement solutions.
