AI Engineer
Tasks
- Build Retrieval Augmented Generation systems
- Build automated evaluation pipelines
- Create context engineering pipelines
- Design agentic AI workflows
- Develop backend AI services and APIs
- Develop multi-agent workflows
- Document system behavior and failure modes
- Establish evaluation frameworks
- Implement observability and analytics
- Implement safety guardrails and fallback logic
- Implement tool using agents
- Integrate AI into microservices and event driven architectures
- Integrate LLM APIs
- Prototype AI first solutions
- Set up online monitoring metrics
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | APIs | Adversarial Testing | Agent systems | Agentic AI | Anthropic API | Azure OpenAI | B testing | Backend Services | CI/CD | Context Budgeting | Context engineering | Cost Tracking | Debugging | Driven systems | Embeddings | Evaluation | Event Driven | Event Driven Systems | Event-driven architecture | Fallbacks | Function Calling | Gemini | Golden Dataset | Grounding | Human-in-the-loop | LLM | LLM-as-judge | Long Context | Microservices | Multi-Agent | Multi-Agent Systems | Observability | Online metrics | OpenAI API | OpenTelemetry | Prompt versioning | Python | RAG | Regression testing | Retries | Retrieval-Augmented Generation | Safety Guardrails | Schema Validation | Search | Summarization | The Loop | Tool-Calling | Tracing | Vector Databases | Vertex AI
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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