Sr. Data Engineer - AI ML
Tasks
- Design build and optimize data pipelines for generative AI systems
- Develop and manage knowledge bases vector stores and graph databases
- Document schemas pipelines and data models
- Enforce compliance access control and responsible AI data handling
- Engineer retrieval augmented reasoning and retrieval augmented generation pipelines
- Ensure data quality consistency and traceability with schema validation metadata tagging lineage
- Evaluate new AI data technologies for graph reasoning and embedding architectures
- Implement feedback and reinforcement loops for agent knowledge validation
- Ingest and transform structured and unstructured content into searchable stores
- Integrate monitoring and observability for retrieval performance and model data alignment
Perks/Benefits
- Comprehensive benefits package
- Flexible work options
- Hybrid work model
- Life leave days
- Professional development days
- Remote first work options
- Vacation time increasing with tenure
- Volunteer days
Skills/Tech-stack
AWS ECS | AWS Glue | AWS Lambda | AWS Neptune | AWS Step Functions | Access Control | Airflow | Amazon Redshift | Amazon S3 | ArangoDB | Athena | Data Governance | Data Lineage | Data Modeling | Dspy | Elastic Vector Search | Embeddings | Faiss | Graph Databases | Infrastructure as Code | Knowledge graphs | LLM Prompt | LLM prompt evaluation | LangChain Agents | Langchain | Llamaindex | MCP | Milvus | Model Context Protocol | Monitoring | Neo4j | Observability | Pinecone | Prefect | Prompt evaluation | Python | RAG | RAQ | Retrieval-Augmented Generation | Retrieval-augmented reasoning | SQL | Schema Design | Semantic Kernel | Semantic Search | Step Functions | Terraform | Text Chunking | Vector Databases | Vector Search | Weaviate | “as-code”
Education
N/A
Roles
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