AI/ML Engineer
Tasks
- Apply LLMOps and MLOps best practices
- Build AI pipelines
- Build Agentic AI workflows
- Build LLM-powered applications
- Continuously improve production accuracy reliability and cost
- Deploy machine learning models
- Design machine learning models
- Document models and workflows
- Ensure cloud governance security compliance
- Evaluate machine learning models
- Implement orchestration patterns
- Implement prompting and structured outputs
- Implement retrieval-augmented generation patterns
- Implement tool calling patterns
- Maintain reproducible deployments
- Manage machine learning models
- Monitor LLM applications in production
- Monitor data preparation pipelines
- Optimize machine learning models
- Orchestrate autonomous tasks
- Prepare data for AI workflows
- Run model experimentation
- Track model lifecycle with MLflow
- Troubleshoot AI workflows
Perks/Benefits
- N/A
Skills/Tech-stack
AI Foundry | Active Directory | Azure AI | Azure AI Foundry | Azure Active Directory | Azure Data | Azure Data Factory | Azure Databricks | Azure Machine Learning | Azure Virtual | Azure Virtual Machines | CI/CD | Data Factory | Data Pipelines | Docker | ETL | Experiment tracking | GitLab | Kubernetes | LLM | LLMOps | Langchain | Langgraph | Langsmith | Language Models | Large Language Models | MCP | MLOps | MLflow | Machine Learning | Model Context Protocol | Model Evaluation | Orchestration | Prompt engineering | PyTorch | Pydantic | Python | RAG | Retrieval | Retrieval-Augmented Generation | Tool-Calling | Vector Search | Virtual Machines
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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