Applied AI Engineer
Tasks
- Build data pipelines
- Collaborate on backend service contracts
- Control quality of model outputs
- Define human approval workflows
- Design and build production AI systems
- Design evaluation and regression detection
- Design system safety safeguards
- Implement tenant isolation and auditing
- Implement tool use and function calling
- Integrate large language models in production
- Manage LLM cost and latency
- Mentor engineers in applied AI practices
- Perform prompt engineering
- Select solution type for each problem
- Translate ambiguous business problems into measurable solutions
Perks/Benefits
Skills/Tech-stack
API Integration | Amazon Web Services | Cloud platform | Cost Optimization | Deep learning | Docker | Evaluation metrics | Function Calling | Go | Google Cloud | Google Cloud Platform | Human-in-the-loop | JAX | Kafka | Kubernetes | LLM Evaluation | Latency optimization | Machine Learning | Microsoft Azure | Multi-tenant | Multi-tenant architecture | Node.js | Prompt engineering | Pub/Sub | PyTorch | Python | RAG | Regression testing | Retrieval-Augmented Generation | Rust | Scikit-learn | Security safeguards | Tenant architecture | The Loop | Tool use | TypeScript | Web Services
Education
N/A
Regions
Countries
States
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