AI/Machine Learning Engineer
Tasks
- Build AI agents and automation workflows
- Build LLM applications
- Build demand forecasting models
- Create runbooks and model cards
- Design AI/ML solutions
- Design lakehouse data products
- Develop Python ETL/ELT pipelines
- Implement MLOps CI/CD pipelines
- Implement backtesting and drift detection
- Implement rag pipelines
- Implement responsible AI governance
- Integrate AI into Power Platform solutions
- Manage AI context and interoperability
- Mentor developers on AI use
- Provision environments with infrastructure as code
- Set up observability and telemetry
- Train and monitor machine learning models
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AI Foundry | Agile | App Insights | Azure AI | Azure AI Foundry | Azure Cognitive | Azure Cognitive Search | Azure Cognitive Services | Azure DevOps | Azure Machine Learning | Azure Synapse | Azure Synapse Analytics | B testing | Bicep | CI/CD | Canary deployments | Cognitive Search | Cognitive Services | Copilot Studio | Databricks | Deep learning | ELT | ETL | Feature Engineering | Fine Tuning | Git | Graph API | Jira | Lakehouse | Language Models | Large Language Models | MLOps | Machine Learning | Microsoft Fabric | Model Monitoring | OpenAI on Azure | Power Platform | Prometheus | Prompt engineering | Python | RBAC | Retrieval-Augmented Generation | Semantic Search | Series forecasting | Streaming Data | Synapse Analytics | Terraform | Time Series | Time Series Forecasting | Vector embeddings
Education
N/A
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