AI Engineer
Tasks
- Architect and deploy AI services in private cloud environments
- Architect and implement RAG systems
- Build AI powered applications using enterprise LLMs
- Build CI/CD pipelines
- Continuously evaluate emerging AI tools and techniques
- Evaluate and integrate frontier models
- Ground AI responses in proprietary data
- Implement AIOps and MLOps best practices
- Monitor version and automate testing
- Own AI development lifecycle from ideation to production
- Research and build agentic AI workflows
Perks/Benefits
Skills/Tech-stack
AI Services | AIOps | Azure | Azure AI | Azure AI Services | Azure DevOps | Azure Key Vault | CI/CD | Chunking | Context window | Context window management | Copilot Studio | Docker | Embedding strategies | Embeddings | Function/tool calling | Key Vault | Langchain | Langgraph | Language Models | Large Language Models | MLOps | Machine Learning | Managed Identities | Private Endpoints | Prompt engineering | Python | RAG | Retrieval evaluation | Retrieval-Augmented Generation | Structured Output | Tool-Calling | VNet | Vector Databases | Window management
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
Regions
Countries
States
Cities
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