Generative AI Developer
Tasks
- Define architectural patterns and technical standards
- Design build deploy agentic ai solutions
- Design build deploy end to end generative ai solutions
- Design integrations with enterprise data platforms knowledge sources and apis
- Develop agentic solutions using Microsoft Copilot Studio
- Document architectural patterns methodologies and lessons learned
- Establish governance for model usage prompt tool management safety controls
- Evaluate emerging generative ai technologies and recommend adoption strategies
- Integrate llms and autonomous agents into solution pipelines
- Manage llm model lifecycle evaluation deployment monitoring
- Review infrastructure approaches containerization orchestration infrastructure as code
Perks/Benefits
- N/A
Skills/Tech-stack
AI Foundry | APIs | Agent systems | Agentic AI | Azure | Azure AI | Azure AI Foundry | Containerization | Copilot Studio | Data integration | Generative AI | Governance | Infrastructure as Code | Language Models | Large Language Models | Machine Learning | Microsoft Copilot | Microsoft Copilot Studio | Model Evaluation | Model Monitoring | Multi-Agent | Multi-Agent Systems | Orchestration | Privacy | Retrieval-Augmented Generation | Security | “as-code”
Education
Roles
AI | AI Developer | AI Solutions | AI Solutions Architect | Architect | Developer | Generative AI Developer | Solutions Architect
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