Senior AI Data Engineer
Tasks
- Build ETL processes for AI research
- Build streaming and event driven pipelines
- Create knowledge graph data systems
- Design scalable data pipelines
- Develop scalable data models
- Develop vector embedding storage and retrieval
- Enable data lineage tracking
- Establish observability and monitoring
- Implement automated quality checks for training data
- Implement data governance and security
- Implement data validation and schema testing
- Monitor data compliance with privacy regulations
- Optimize data partitioning and storage
- Optimize pipeline performance for low latency
- Support real time telemetry pipelines
- Troubleshoot data pipeline failures
Perks/Benefits
- N/A
Skills/Tech-stack
Amazon Web Services | Apache Airflow | Apache Flink | Apache Kafka | Apache Spark | CI/CD | Cloud platform | Dask | Data Governance | Data Lakehouse | Data Lineage | Data Observability | Data Partitioning | Data Security | Data Validation | Docker | Docker Compose | ETL | Event Driven | Event-driven architecture | Experiment tracking | Feature Store | Fine Tuning | Google Cloud | Google Cloud Platform | Human Feedback | Indexing | Infrastructure as Code | Knowledge graphs | Kubernetes | LLM Fine-tuning | Learning from Human Feedback | Machine Learning | Microsoft Azure | Milvus | NoSQL | Pinecone | Prompt engineering | PyTorch | Python | RAG | Ray | Reinforcement Learning | Reinforcement Learning from Human Feedback | Retrieval-Augmented Generation | SQL | Scala | Schema Testing | Semantic Caching | Streaming Data | TensorFlow | Terraform | Vector Databases | Vector embeddings | Weaviate | Web Services | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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