Job Description
What you’ll do
Design 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 skills
Strong
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-have
Knowledge 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).
