AI Engineer
Tasks
- Apply context engineering
- Apply spec-driven development
- Build and ship AI-powered features
- Build evaluation frameworks for AI features
- Design and implement RAG pipelines
- Detect AI output regressions
- Develop maintainable backend services for AI
- Ensure AI outputs are observable traceable and auditable
- Implement AI guardrails
- Implement agent-based workflows
- Integrate AI features into existing systems
- Integrate LLMs into production applications
- Perform AI feature prompt testing and quality benchmarking
Perks/Benefits
Skills/Tech-stack
AI Act | Agent workflows | Caching | Cloud Native | Cloud-Native Deployment | Containerization | Content Filtering | Context engineering | Docker | Embeddings | Evaluation Frameworks | Fallback mechanisms | GDPR | Input validation | JavaScript | Knowledge Base | Knowledge Base Retrieval | Knowledge graphs | LLM integration | Model selection | Monitoring | Observability | Output Validation | Prompt engineering | Prompt testing | Python | Retrieval-Augmented Generation | Spec Driven Development | Token optimization | TypeScript | Vector Databases
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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