Responsibilities
- Lead the architecture and implementation of full-cycle AI solutions
- Develop and scale retrieval-augmented generation (RAG) systems and large language model applications
- Design and deploy agentic AI and multi-agent system frameworks
- Create robust, production-ready AI APIs and microservices
- Orchestrate multi-agent workflows using frameworks such as LangGraph, AutoGen, CrewAI, or Semantic Kernel, applying appropriate patterns like ReAct, self-reflection, hierarchical delegation, or plan-and-execute based on use case
- Construct and manage Model Context Protocol (MCP) servers and enable Agent-to-Agent (A2A) integration for enterprise-scale multi-agent coordination
- Deploy and maintain AI applications across cloud infrastructure
- Improve efficiency, response time, and cost-effectiveness of AI systems
- Guide junior engineering staff and lead code and design reviews
- Work with cross-functional teams to convert business needs into functional AI implementations
Compensation
Not specified
Work Arrangement
Not specified
Team
Not specified
Not specified
