Sr. Data Engineer - AI ML
Tasks
- Add monitoring and observability for retrieval performance
- Automate continuous knowledge updates
- Build and manage graph databases
- Create feedback and reinforcement loops
- Design and optimize data pipelines
- Develop knowledge bases and vector stores
- Document schemas pipelines and data models
- Enforce data governance and access control
- Engineer RAG and RAQ pipelines
- Ensure data quality and traceability
- Evaluate new AI data technologies
- Implement ingestion workflows for structured and unstructured data
Perks/Benefits
- Comprehensive benefits packages
- Flexible work options
- Hybrid work model
- Life leave days
- Professional development days
- Retirement savings options
- Vacation time increases with tenure
- Volunteer days
Skills/Tech-stack
AWS Glue | AWS Lambda | AWS Neptune | AWS Step Functions | Amazon Athena | Amazon ECS | Amazon Redshift | Amazon S3 | Apache Airflow | ArangoDB | Context chunking | Data Lineage | Data Modeling | Dspy | Elasticsearch | Embeddings | Faiss | Graph Databases | Infrastructure as Code | Knowledge graphs | LLM | Langchain | Llamaindex | Metadata tagging | Milvus | Model Context Protocol | Neo4j | NoSQL | Orchestration frameworks | Pinecone | Prefect | Prompt evaluation | Python | RAG | Retrieval Orchestration | SQL | Schema Validation | Semantic Kernel | Semantic indexing | Step Functions | Vector Databases | Weaviate | “as-code”
Education
N/A
Roles
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