AI LLM Engineer
Tasks
- Build Python pipelines for training and deployment
- Build evaluation for prompt and model quality
- Build orchestration agent workflows
- Create natural language interfaces
- Deploy ML models for forecasting and anomaly detection
- Design AI ready data layers on Snowflake
- Design build deploy LLM applications
- Develop enterprise copilots
- Ensure responsible AI security and compliance governance
- Implement embeddings and vector retrieval
- Implement fine-tuning and prompt engineering
- Implement rag pipelines
- Integrate AI with enterprise tools using APIs
- Integrate Power BI and Fabric for automated insights
- Integrate tool augmented agents
- Mitigate hallucinations and improve reliability
- Optimize SQL queries for AI systems
- Orchestrate automated reporting and monitoring workflows
Perks/Benefits
- 401k retirement plan
- Dental insurance
- Disability insurance
- Life insurance
- Medical insurance
- Paid parking/public transportation
- Paid sick and safe time
- Paid time off
- Vision insurance
Skills/Tech-stack
AI Foundry | APIs | Agent systems | Anomaly Detection | Azure AI | Azure AI Foundry | Azure OpenAI | Copilot Studio | Data Modeling | Data Warehousing | Databricks | ELT | Embeddings | Feature Store | Forecasting | Langchain | Langgraph | Language Processing | Machine Learning | Microsoft Copilot | Microsoft Copilot Studio | Microsoft Fabric | Model Evaluation | Model Monitoring | Multi-Agent | Multi-Agent Systems | Natural Language | Natural Language Processing | OneLake | Orchestration | Power BI | Prompt engineering | Python | Retrieval-Augmented Generation | SQL | Semantic Kernel | Snowflake | Tool use | Tool-use agents | Vector Databases
Education
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