LLMOps Engineer
Tasks
- Apply prompt protections
- Automate model versioning
- Build and maintain LLM pipelines
- Build monitoring and observability
- Conduct root cause analysis
- Deploy and host model endpoints
- Deploy updated prompts and models safely
- Develop embedding pipelines
- Document workflows and runbooks
- Enable alerting and logging
- Ensure scalability testing and integration
- Filter model outputs
- Implement CI/CD pipelines
- Implement MLSecOps guardrails
- Implement retrieval layers
- Integrate vector databases
- Maintain operational best practices
- Manage containers and microservices
- Manage datasets
- Monitor model outputs and performance
- Optimize inference runtimes
- Perform input validation
- Set up metrics dashboards
- Support automated testing and evaluation
- Support deployment to production
- Track configuration for reproducible development
- Troubleshoot operational incidents
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agile | Alerting | Azure | CD | CI | CI/CD | Cloud platform | Configuration Management | Containerization | Dataset Management | DevSecOps | Docker | Embedding Models | Google Cloud | Google Cloud Platform | Input validation | Kubernetes | LLM Inference | Logging | Metrics | Microservices | Model versioning | Monitoring | Observability | Output filtering | Prompt engineering | Python | RAG | Retrieval-Augmented Generation | Security | Unit Testing | Vector Databases
Education
Roles
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