Applied AI Engineer
Tasks
- Build LLM routing proxy services
- Collaborate on AI system design with product infrastructure and data teams
- Create reusable AI ML tooling
- Design deployment workflows for AI systems
- Develop internal AI APIs
- Implement LLM ML observability and monitoring
- Improve AI service cost latency and safety
- Integrate emerging AI models into platform capabilities
- Optimize AI system performance and reliability
- Perform LLM ML evaluation
- Support testing and scaling of AI systems
Perks/Benefits
- Equipment support
- Healthcare coverage
- Home office stipend
- Hybrid friendly work
- Learning and development budget
- Mental health and wellness resources
- Parental leave
- Remote-first culture
- Unlimited paid time off
Skills/Tech-stack
API Development | Backend Development | Deployment | Evaluation | Infrastructure Engineering | LLM | LLMOps | Latency optimization | MLOps | Machine Learning | Monitoring | Observability | Python | Reliability | Scalability | Testing | TypeScript
Education
N/A
Roles
AI | AI Engineer | Applied AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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