Lead the design and development of advanced AI agent systems, focusing on real-world automation, multi-agent orchestration, and scalable LLM-powered solutions for enterprise use cases.
Preferred / Robust Plus
Experience in B2B/MSME product settings, understanding operational constraints such as cost sensitivity, connectivity limitations, and workflow automation needs.
Familiarity with open-source conversational frameworks such as Rasa, Botpress, or Microsoft Bot Framework.
Experience optimizing LLM inference for latency and cost (quantization, batching, dynamic model routing, caching).
Solid understanding of conversational product metrics (resolution rate, deflection rate, handle time, CSAT) and how to optimize models and pipelines for them.
Additional Bonus Qualifications
Familiarity with Model Context Protocol (MCP)
and designing systems where agents interact with external tools and structured knowledge through MCP-style interfaces.
Experience building agent-to-agent (A2A) systems, multi-agent orchestration, or agent collaboration frameworks for task delegation and workflow automation.
Experience integrating LLM agents with enterprise tools via MCP-compatible tool servers, structured APIs, or workflow orchestration systems.
Knowledge of emerging agent infrastructure ecosystems (tool registries, agent communication protocols, tool-use policies, guardrails).
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