AI LLM Engineer
Tasks
- Build LLM-powered applications
- Build ML models for forecasting and anomaly detection
- Collaborate with platform and governance teams
- Create tool augmented agent workflows
- Design AI ready data layers on Snowflake
- Design RAG pipelines
- Develop AI copilots
- Develop Python pipelines for model development
- Develop prompt evaluation frameworks
- Implement automation using APIs and orchestration tools
- Implement embeddings and vector retrieval
- Integrate AI agents with enterprise systems
- Integrate AI with Power BI and Fabric
- Integrate AI with ServiceNow and Salesforce
- Monitor model performance in production
- Optimize SQL queries for AI use cases
- Orchestrate multi-agent workflows
- Train, evaluate, and deploy ML models
Perks/Benefits
- 401k retirement plan
- Dental insurance
- Life insurance
- Long-term disability
- Medical insurance
- Paid parking
- Paid sick and safe time
- Paid time off
- Short-term disability
- Vision insurance
Skills/Tech-stack
AI Foundry | APIs | Agent systems | Anomaly Detection | Azure AI | Azure AI Foundry | Azure OpenAI | Copilot Studio | Data Modeling | Data Quality | Data Warehousing | Data quality monitoring | Databricks | ELT | Embeddings | Feature Store | Fine Tuning | Forecasting | Langchain | Langgraph | Language Models | Large Language Models | Machine Learning | Microsoft Copilot | Microsoft Copilot Studio | Microsoft Fabric | Model Deployment | Model Monitoring | Multi-Agent | Multi-Agent Systems | OneLake | Power BI | Predictive Analytics | Prompt engineering | Prompt evaluation | Python | Quality monitoring | RAG | Retrieval-Augmented Generation | SQL | Salesforce | Semantic Kernel | ServiceNow | Snowflake | Vector Databases | Vector Search | Vector index
Education
Roles
AI | AI/LLM Engineer | Data Scientist | Engineer | LLM Engineer | Learning Engineer | Machine Learning Engineer | Scientist
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