Sr. Consultant Machine Learning & Knowledge Graph Engineer
Tasks
- Architect graph backed retrieval augmented generation systems
- Build tool calling interfaces and prompt to graph query pipelines
- Define and govern enterprise ontologies and semantic schemas
- Design and scale knowledge graph platforms
- Design real time graph ingestion pipelines
- Design virtualized graph layers for query federation
- Drive MLOps standards and build production grade machine learning services
- Establish graph data governance frameworks
- Implement RBAC based graph security models
- Implement SHACL and SHEX validation
- Lead architecture development deployment of enterprise machine learning solutions
- Mentor engineers and lead engineering culture
- Operationalize graph algorithms and graph analytics
- Recommend adoption of emerging graph technologies
- Track data lineage and manage ontology versioning
Perks/Benefits
- N/A
Skills/Tech-stack
APOC | Agentic AI | Airflow | Apache Iceberg | AuraDB | CDC | CI/CD | Causal Clustering | Centrality analysis | Community Detection | Containerization | Cypher | Data Lakehouse | Data Science | Data virtualization | Delta Lake | Docker | FastRP | Federation | GQL | Graph Data | Graph Data Science | Graph Neural Networks | Graph Search | Hybrid Vector Graph Search | ISO Standards | Kafka | Knowledge graph | Label Propagation | Link prediction | Louvain | MLOps | Machine Learning | Natural Language | Natural Language to Query | Neo4j | Neo4j GDS | Neural Networks | Node2vec | OWL 2 | Ontology Engineering | PageRank | PySpark | Python | RAG | RDF | RDFS | Retrieval-Augmented Generation | SHEX | SPARQL | SQL | Semantic Modeling | Shacl | Spark Structured Streaming | Stardog | Stardog Virtual Graphs | Structured Streaming | Testing | Tool-Calling | Vector Search | Version control | Virtual Graphs
Education
N/A
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