Description:
Key Responsibilities
- Build and maintain LLM driven enterprise level applications in real production environment.
- Design and develop MCP tools using Python, FastAPI, or similar frameworks
· Maintain multiple MCP services and LLM integrations.
· Develop Angular-based conversational user interfaces with real-time updates.
- Design scalable architectures capable of supporting high concurrency.
- Implement monitoring, logging, and tracing for AI workflows.
- Continuously optimize latency, cost, and response quality.
- Implement and support multi-agent architectures with context sharing and orchestration
- Implement WebSocket-based communication for streaming AI responses
- Use Redis for caching, session management, and conversation memory
- Maintain scalable services for conversation state, workflow, and memory handling
· Strong understanding of API contracts and schema-driven development.
- Understanding of Prompt engineering, Structured outputs, Tool calling patterns, Model limitations and failure modes.
· Good Understanding of Agent orchestration patterns, Role-based agents, Task decomposition, Coordination and fallback mechanisms.
Technical Skills
- Frontend:
- Angular / React
- RxJS and reactive programming
- Real-time UI updates using WebSockets
- Backend:
- Python
- FastAPI / async services
- API and middleware development
- AI & Platform:
- LLM integrations and tool calling
- MCP / FastMCP
- LangChain, LangGraph
- Multi-agent architectures
- Systems & Infrastructure:
- Distributed systems and scalability concepts
- WebSockets, event-driven systems
- Redis (caching, session, memory)
- Databases: Cosmos DB, MongoDB
- Vector databases: Pinecone or similar
- Cloud & DevOps:
- Azure, Azure AI Foundry
- Azure GitHub (PRs, branching, CI/CD pipelines)