Data and Analytics - AI Engineer II
Tasks
- Apply AIOps and MLOps practices
- Build and deploy RAG copilots
- Build and deploy production ready AI applications
- Contribute to enterprise AI strategy and adoption
- Design and develop AI/ML solutions
- Design and integrate agentic processes
- Develop agentic workflows and intelligent automation
- Develop data pipelines and infrastructure
- Ensure AI security compliance and governance
- Implement APIs and services for agent integration
- Maintain and optimize data pipelines for training and inference
- Monitor model accuracy drift and metrics
- Monitor model performance and implement improvements
- Participate in code reviews and documentation
- Promote responsible AI fairness explainability and compliance
- Provide continuous monitoring and lifecycle management
Perks/Benefits
- Continuous learning
- Hybrid work
- On site collaboration as needed
- Professional growth
- Supportive work environment
Skills/Tech-stack
AI Foundry | AIOps | APIs | Azure | Azure AI | Azure AI Foundry | C# | Data Pipelines | Deep learning | Explainability | Fairness | ML.NET | MLOps | Machine Learning | Microsoft Fabric | Model Deployment | Model Drift | Model Monitoring | NLP | Power Automate | Power Platform | PyTorch | Python | RAG | Responsible AI | SQL | SQL Server | Snowflake | TensorFlow
Education
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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