Your responsibilities

Own the end-to-end graph platform lifecycle: architecture, deployment, reliability, and continuous evolution.
Design graph schemas and ontologies across LPG and RDF paradigms, ensuring standards-based governance and version control.
Build and operate scalable data ingestion pipelines (batch and streaming) with data quality and lineage frameworks.
Implement robust observability and reliability practices aligned with SRE principles on GCP (monitoring, alerting, HA).
Define engineering guardrails for performance tuning, query optimization, and cost-efficient scaling.
Collaborate with product, data, and security stakeholders to deliver reusable platform services for internal teams.
Activate AI-driven reasoning and retrieval scenarios leveraging LLMs, embeddings, vector databases, and hybrid GraphRAG workflows.
Note: Detailed project information will be shared during the recruitment process.

Our requirements

3+ years in data/database engineering, including graph platform implementation.
Experienced in both major graph ecosystems:
LPG stack: Cypher/Gremlin; platforms like Neo4j, FalkorDB, JanusGraph.
RDF stack: SPARQL; platforms like GraphDB, Stardog, Blazegraph, Neptune RDF.
Proficiency in graph standards (RDF, RDFS, OWL, SHACL) and ontology governance/versioning best practices.
Hands-on delivery experience on Google Cloud Platform (GCP) including GKE, Cloud Run, Pub/Sub, Dataflow, BigQuery, IAM, dashboards/monitoring.
Practical LLM integration exposure (embeddings, vector/hybrid retrieval, GraphRAG workflows).
Strong foundations in Python and/or Java/Scala/Node.js, API integration, CI/CD pipelines, and Infrastructure-as-Code (Terraform, GitOps).
Proven track record in leading engineering efforts and delivering iterative platform roadmaps.
Clear communication across technical and non-technical stakeholders, ability to translate business objectives into platform capabilities.
Commitment to engineering excellence, proactive incident management, and continuous improvement culture.

Multi-tenant architecture and platform productization experience.
Knowledge of responsible AI practices, model guardrails, and evaluation frameworks.
Experience with Kubernetes operators and advanced observability stacks (Prometheus, Grafana, OpenTelemetry).
Familiarity with FinOps strategies for high-memory graph workloads.

Flexible cooperation model
Hybrid work setup – 8 times per month from the office
Collaborative team culture – work alongside experienced professionals eager to share knowledge

Continuous development – access to training platforms and growth opportunities
Comprehensive benefits – including Interpolska Health Care, Multisport card, Warta Insurance, and more
High quality equipment – laptop and essential software provided