Description

Key Responsibilities

• Design and implement multi-agent orchestration frameworks including autonomous negotiation, task delegation, state management, and secure inter-agent communication
• Architect and build scalable Model Context Protocol (MCP) servers and clients to connect LLMs to data sources, tools, and enterprise environments
• Drive the architectural vision for an internal AI development platform (high throughput, low latency, seamless developer workflows)
• Write production-ready code, build prototypes, and establish engineering best practices for AI application development
• Standardize architectural patterns organization-wide; mentor junior engineers; communicate technical concepts to non-technical stakeholders

Required Skills

• Multi-agent frameworks: LangGraph, AutoGen, CrewAI
• Model Context Protocol (MCP) or similar context-sharing/tool-calling protocols
• RAG pipelines, vector databases, prompt engineering
• Programming: Python, TypeScript, or Go
• Cloud-native: AWS, GCP, or Azure; Kubernetes, Docker
• Modern AI SDKs and LLM application development

Key Skills

Education

Bachelor's degree