Business Area: ITSeniority Level: Mid-Senior level : At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world's largest enterprises.

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About the Team & Role We are engineering an enterprise-grade Everything-as-Code (Ea C) AI-First Platform that transforms modern enterprise operations through automated delivery pipelines, machine-readable specifications, and agentic intelligence. As a Graph RAG Engineer, you will own the semantic, vector, and graph storage layer powering the core context engine for our enterprise AI utilities and Internal Developer Portal.

Operating at the intersection of modern database administration, distributed event streaming, and generative AI pipelining, you will bridge our AWS MSK event mesh with downstream knowledge graphs and vector engines across AWS and GCP. You will lead the deployment of our SDLC Context Graph and Graph RAG Engine, enabling automated Change Advisory Board (CAB) compliance, semantic code/schema lineage tracking, and enterprise LLM proxy integrations.

As a Graph RAG Engineer you will: Graph & Vector Database Infrastructure: Provision, tune, and maintain production-grade clusters for Graph databases (Neo4j using Cypher, APOC, and causal clustering) and Vector storage engines (pgvector on Postgre SQL / AWS/GCP managed storage).



Engineer high-throughput index structures, cosine similarity vector indexes, and query optimizations for sub-second responses. SDLC Context Graph & Lineage Pipelines: Build automated ingestion pipelines to parse Git repositories, ASTs, Jira issue links, Apache Avro schemas, and CI/CD metadata into a unified enterprise knowledge graph.

Graph RAG Orchestration & Agentic Search: Connect distributed pipeline engines to hydrate hybrid retrievers (combining structured SQL, Cypher graph traversals, and dense vector embeddings) for AI-driven developer workflows and autonomous coding agents. Cyclic Agent Safeguards & Governance: Configure circuit breakers, confidence scoring thresholds, and step-limit constraints to restrict autonomous cyclic agent execution, protect token budgets, and prevent runaway execution loops.

Prompts-as-Code & Enterprise LLM Gateway Integration: Integrate microservices and knowledge stores with the central Enterprise AI Gateway, maintaining version-controlled system prompt structures inside / spoke directories while adhering to DLP PII scrubbing rules and token rate limits. High Availability & Fin Ops: Implement automated failover, backup restoration, and multi-cloud storage tier cost controls across AWS and GCP environments.

We are excited if you have (Required Technical Expertise): Graph Databases:



Deep operational and development experience with Neo4j (Cypher, APOC, causal clustering) or enterprise Knowledge Graphs.

Vector Search & RAG: Proven expertise with pgvector (Postgre SQL), embeddings management, hybrid search techniques, and framework integrations (Lang Chain, Llama Index, or custom RAG pipelines).

Database

Administration & Cloud Storage: Hands-on experience managing relational (Postgre SQL) and graph databases across AWS and GCP cloud environments. Data Pipelining & Streaming: Proficiency in consuming Apache Avro payloads, streaming Kafka events (AWS MSK), and parsing structured/unstructured code and JSON artifacts.

Agentic AI & Prompt Engineering: Practical understanding of Prompts-as-Code patterns, few-shot prompt optimization, and agent tool specification. You may also have: Experience with Infrastructure-as-Code (Terraform) primitives, Kubernetes (EKS/GKE), Docker, and pull-based Git Ops workflows. Exposure to Hashi Corp Vault Transit encryption, OIDC keyless authentication, and zero-trust workload identities. xugodme

Familiarity with Open Telemetry (OTel) instrumentation for tracking vector search query latencies and LLM inference performance in Datadog or Grafana.

What you can expect from us: Generous PTO Policy Support work life balance with Unplugged Days Adaptable WFH Policy Mental & Physical Wellness programs Phone and Internet Reimbursement program Access to Continued Career Development Comprehensive Benefits and Competitive Packages Paid Volunteer Time Employee Resource Groups EEO/VEVRAA Location: Hungary-Remote; Poland-Remote; Spain-Other-Remote; Czech Republic > Remote; Spain-Barcelona-Remote; Spain-Madrid-Remote Type: Full time

📌 Graphrag engineer (Arenas de Iguña)
🏢 Cloudera
📍 Arenas de Iguña