Senior AI Data Engineer
Tasks
- Build ETL processes for AI research
- Build real-time telemetry pipelines
- Build streaming and event driven pipelines
- Collaborate on data requirements and quality
- Create vector embeddings storage and retrieval
- Design scalable data pipelines
- Enable LLM RAG data pipelines
- Implement data governance and security
- Implement data validation and schema testing
- Model data for analytics and ML training
- Optimize cloud data platform reliability scalability cost
- Optimize partitioning indexing and storage
- Set up observability and monitoring
- Track data lineage
- Troubleshoot data pipeline failures
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Airflow | Automated Data Validation | Azure | CI/CD | Dask | Data Governance | Data Lakehouse | Data Lineage | Data Pipelines | Data Security | Data Validation | Docker | ETL | Event Driven | Event-driven architecture | Experiment tracking | Feature Engineering | Feature Store | Flink | GCP | Hybrid Architecture | Kafka | Knowledge graphs | Kubernetes | LLM | LLM Evaluation | Milvus | Monitoring | NoSQL | Observability | Pinecone | Prompt engineering | PyTorch | Python | RAG | Ray | Retrieval-Augmented Generation | SQL | Scala | Schema Testing | Semantic Caching | Spark | Streaming | TensorFlow | Terraform | Vector Databases | Vector embeddings | Weaviate
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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