【27届校招提前批】算法工程师
Tasks
- Align LLM responses with business requirements
- Assess suitability for production use
- Build LLM agent systems
- Create reusable agent infrastructure and best practices
- Deploy and iterate LLM Agent solutions
- Design agent architecture
- Develop Agent harness and evaluation framework
- Implement RAG systems
- Optimize LLM output with prompt engineering
- Track and evaluate new LLM and Agent technologies
Perks/Benefits
- N/A
Skills/Tech-stack
Agent collaboration | Asynchronous programming | Caching | Concurrency | Context engineering | DPO | Distributed Systems | Distributed Training | Docker | Fine Tuning | Function Calling | Git | LLM Evaluation | Linux | Memory | Message Queues | Model Fine-tuning | Multi-Agent | Multi-agent collaboration | NoSQL | Planning | Prompt engineering | Python | RAG | RESTful API | RLHF | SFT | SQL | Tool use | Web development
Education
Roles
AI | AI Engineer | Algorithm Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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