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High demand for GenAI roles but niche agentic framework expertise narrows applicant pool.
Specialized agentic AI, LLM tooling, and enterprise integration skills limit cross-industry transferability.
Extensive mandatory technical stack (LangChain, MCP, vector DBs, cloud deployment) enforces strict technical filters.
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Build and deploy agentic AI solutions including tool-using LLM agents, autonomous workflows, and multi-agent systems.
Translate business problems into scalable, production-ready AI automations in collaboration with AI Architects and domain experts.
Implement and operate stateful, multi-agent systems integrated with Azure AI and multi-cloud platforms under enterprise governance.
Experience with agentic AI and orchestration frameworks like LangChain, LangGraph, Microsoft Foundry, or equivalents.
Proficiency in LLM integration, tuning, orchestration, and containerized deployment on cloud platforms such as Azure, AWS, or GCP.
Hands-on knowledge of MCP, A2A workflows, swarm agent architecture, vector databases, tool/function calling pipelines, and logging/tracing for agent actions.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in engineering multi-agent AI systems within enterprise and cloud-governed environments.
Strong practical skills with modern agentic AI frameworks and cloud-native deployment.
Able to collaborate with architects and domain experts to convert business needs into AI-driven automation solutions.