Data and Analytics - AI Engineer II
Tasks
- Apply AIOps and MLOps practices
- Apply responsible AI fairness explainability compliance
- Build and deploy RAG augmented copilots
- Build prototypes MVPs and production AI applications
- Collaborate with cross-functional teams to deliver scalable solutions
- Contribute to enterprise AI strategy and adoption
- Design AI ML solutions aligned with business objectives
- Design and integrate agentic processes into workflows
- Develop agentic workflows and intelligent automation solutions
- Develop data pipelines for AI workloads
- Ensure AI security compliance governance
- Implement APIs and services for agent integration
- Maintain and optimize training and inference pipelines
- Monitor model accuracy drift and performance metrics
- Monitor model performance implement continuous improvements
- Partner with data IT and business stakeholders
- Perform code reviews and documentation
Perks/Benefits
- Continuous learning
- Hybrid work environment
- Insurance industry exposure
- Professional growth
- Supportive collaborative culture
Skills/Tech-stack
AI Foundry | AIOps | APIs | Agentic Workflows | Azure | Azure AI | Azure AI Foundry | C# | Deep learning | Intelligent Automation | ML.NET | MLOps | Machine Learning | Microsoft Fabric | NLP | Power Automate | Power Platform | PyTorch | Python | RAG | 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 | AI Engineer II | Engineer | Engineer II | Learning Engineer | Machine Learning Engineer
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