Ai 应用研发工程师
Tasks
- Analyze AI application requirements
- Build reusable components SDK and standards
- Conduct technical reviews and code review
- Control cost and optimize inference
- Create RAG engine prompt management and evaluation platform
- Create low cost inference services
- Design AI solution
- Develop AI applications
- Develop AI engineering platform and tools
- Implement RAG intelligent dialogue and agents
- Implement deployment pipeline
- Improve performance and concurrency
- Optimize vector database and embeddings
- Select AI technologies
- Set up monitoring and alerting
- Track industry AI trends and apply to business
- Tune prompts and reduce hallucinations
Perks/Benefits
- N/A
Skills/Tech-stack
API Development | Agent | Alerting | CI/CD | Docker | Embeddings | Function Calling | Hallucination mitigation | Inference Optimization | Java | MLOps | Message Queue | Model Deployment | Model Evaluation | Monitoring | Multi-turn dialogue | Multimodal AI | MySQL | Prompt Optimization | Prompt engineering | Python | RAG | Redis | SDK development | Spring Boot | Vector Database
Education
N/A
Roles
AI | AI Application Engineer | Application Engineer | Backend | Backend Engineer | Engineer
Regions
Countries
States
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