Applied AI Engineer - US
Tasks
- Build agent based systems in production
- Build agent workflows and orchestration
- Create evaluation frameworks for AI outputs
- Debug production issues
- Define reliability and performance improvements
- Design RAG and memory systems
- Design develop deploy LLM powered applications
- Develop prompt strategies and context management
- Implement backend and frontend features
- Implement unit, integration, and end-to-end testing
- Integrate and optimize LLM APIs
- Optimize latency cost and scalability
- Own product lifecycle concept to production
- Test validate and safely integrate AI generated code
Perks/Benefits
Skills/Tech-stack
Agent Orchestration | Agent workflow | Anthropic API | Benchmarking | CI/CD | Continuous Delivery | Continuous Integration/Continuous Delivery | Continuous integration | Cost Optimization | Debugging | End to End | End-to-End Testing | Fine Tuning | Gemini API | Google Gemini | Google Gemini API | Integration Testing | LLM API | Langchain | Langgraph | Language Models | Large Language Models | Latency optimization | Model Evaluation | OpenAI API | Prompt engineering | RAG | Retrieval-Augmented Generation | Scalability | Unit Testing
Education
Bachelor of Arts | Bachelor of Engineering | Bachelor of Science
Roles
AI Engineer | Applied AI Engineer | Engineer | Full Stack | Full Stack Engineer | Stack Engineer
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