Data and Analytics - AI Engineer II
Tasks
- Apply AIOps and MLOps practices
- Build and deploy RAG augmented copilots
- Build prototypes MVPs and production AI applications
- Collaborate with cross-functional teams
- Conduct code reviews and documentation
- Contribute to enterprise AI strategy and adoption
- Design agentic processes for enterprise systems
- Design and develop AI/ML solutions
- Develop agentic workflows and intelligent automation
- Develop data pipelines for AI workloads
- Ensure AI security compliance and governance
- Implement APIs and services for agent integration
- Implement deployment monitoring and lifecycle management
- Maintain and optimize data pipelines for training and inference
- Monitor accuracy drift and performance metrics
- Monitor model performance and implement improvements
- Partner with data teams IT and stakeholders
- Promote responsible AI fairness explainability and compliance
- Stay current with emerging AI tools
Perks/Benefits
- Collaborative environment
- Continuous learning
- Hybrid work
- On site collaboration as needed
- Professional growth
Skills/Tech-stack
AI Foundry | AIOps | APIs | Azure | C# | Data Pipelines | Deep learning | Explainability | Fairness | Governance | ML.NET | MLOps | Machine Learning | Microsoft Fabric | Model Drift | Model Monitoring | NLP | Power Automate | Power Platform | PyTorch | Python | RAG | Responsible AI | SQL | SQL Server | Snowflake | TensorFlow
Education
Bachelor of Engineering | Bachelor of Science | Bachelor of Science in Computer Science | Bachelor of Science in Data Science
Roles
AI | AI Engineer | Engineer
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