AI & Analytics Engineer
Tasks
- Architect LLM-powered applications
- Build RAG pipelines
- Build telemetry and dosimetry data pipelines
- Deploy and monitor AI models in production
- Design end-to-end AI solutions
- Develop production-grade APIs
- Implement production observability for AI systems
- Integrate ML models into data platform
- Maintain Delta Lake datasets
- Manage model lifecycle with MLflow
- Monitor latency data drift and model degradation
- Orchestrate inference pipelines
Perks/Benefits
Skills/Tech-stack
AI Agents | Async Programming | Azure | Blob Storage | Container Apps | Data Drift | Data drift monitoring | Databricks | Delta Lake | Dimension modeling | Drift monitoring | Event Hubs | Fact-Dimension Modeling | Fact/Dimension | FastAPI | Function Calling | Incremental pipelines | LLM | MLflow | Model Monitoring | OpenAPI | PySpark | Pydantic | Python | RAG | REST | SQL | Service Bus | Structured Output | Tool use
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Data Engineer | Engineer | Learning Engineer | Machine Learning Engineer | Software Engineer
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