Principal AI Engineer
Tasks
- Architect graph based agent workflows
- Build hybrid intelligence patterns
- Build production FastAPI applications
- Design autonomous multi-agent workflows
- Develop reusable agent node libraries
- Ensure error handling and compliance with responsible AI
- Implement agent evaluation frameworks
- Implement enterprise security integration
- Implement prompt management systems
- Implement real time agent streaming with SSE and WebSocket
- Implement response transformation pipelines
- Implement tool calling protocols
- Integrate PostgreSQL and Redis
- Integrate RESTful and event driven APIs
- Integrate cloud LLM providers
- Integrate observability platforms
- Mentor engineers and contribute to documentation
- Optimize token usage and LLM costs
- Persist conversation state with Redis checkpointing
- Translate requirements into scalable agent workflows
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Agent systems | Asyncio | Autogen | Autonomous Agents | Azure | CI/CD | CrewAI | Docker | End to End | End-to-End Testing | Event Driven | Event-driven architecture | FastAPI | Function Calling | Git | Google Cloud | Integration Testing | LLMOps | Langchain | Langfuse | Langgraph | Langsmith | Language Models | Large Language Models | MCP | Model Context Protocol | Multi-Agent | Multi-Agent Systems | Observability | Plan-and-Execute | PostgreSQL | Prompt engineering | Prompt management | Python | REST API | React | Redis | SSE | Unit Testing | WebSocket | Webhooks
Education
Roles
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