Principal, AI Engineer
Tasks
- Architect and implement data pipelines
- Develop AI data integration roadmap
- Drive semantic architecture and context engineering
- Ensure data privacy encryption and access controls
- Guide regulatory compliance audits and reviews
- Integrate structured and unstructured data
- Lead development of client facing AI capabilities
- Manage metadata data lineage and audit trails
- Mentor and coach engineers and architects
- Optimize data workflows for performance reliability and cost
- Provide end to end data integration product management vision
- Provide leadership for technical delivery teams
- Support real-time and batch data ingestion
Perks/Benefits
- N/A
Skills/Tech-stack
.NET | AI Foundry | AWS | Access Control | Agents | Audit trails | Authentication | Azure | Azure AI | Azure AI Foundry | Bash | C# | Container Architecture | Data Governance | Data Ingestion | Data Lineage | Data Pipelines | Data Privacy | Data governance standards | Databricks | Docker | Encryption | Error Handling | Exadata | Function Calling | GCP | GitHub Copilot | Governance standards | LLM | Linux | Metadata Management | Model Risk Management | Model risk | NLP2SQL | Neo4j | Oracle | Orchestration | PGVector | Pinecone | PostgreSQL | Prompt engineering | PySpark | Python | RAG | REST APIs | Rate Limiting | React | Risk Management | SQL | Semantic architecture | Snowflake | Streaming | Tool use | Unix | Vector Database | Windows
Education
Roles
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