AI Data Engineer
Tasks
- Build RAG and machine learning data sets
- Create indexing workflows for retrieval ready datasets
- Design data pipelines for AI and machine learning
- Develop document ingestion and processing pipelines
- Implement chunking strategies and embedding pipelines
- Implement data quality checks and automated validation
- Integrate vector databases and search indexes
- Monitor data pipeline observability and lineage
- Prepare knowledge graph data pipelines
- Transform data for machine learning feature engineering
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | Chunking | Cloud Data | Cloud Data Platforms | DBT | Dagster | Data Contracts | Data Governance | Data Lineage | Data Modeling | Data Quality | Data Warehouse | Data platforms | Dataset versioning | Document parsing | ELT | ETL | Elasticsearch | Embeddings | Entity relationship | Entity-Relationship Extraction | Feature Engineering | Feature Store | Graph Data | Graph Data Modeling | Graph Databases | Hybrid search | Lakehouse | Milvus | Model Training | Model Training Data | Monitoring | Neo4j | Object storage | Observability | OpenSearch | PGVector | Pandas | Pinecone | Prefect | PySpark | Python | RAG | RDF | Relational databases | Relationship Extraction | SPARQL | SQL | Semantic Search | Training data | Vector Database | Weaviate
Education
N/A
Roles
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