Consultant AI agent engineer
Budget: $30 – $250 USD
We are looking for an experienced AI engineer or consultant to help us design and build a robust multi-agent engine for our SaaS platform. The primary goal is to architect and implement a reliable, efficient, and cost-controlled multi-agent system capable of orchestrating a supervisor agent and multiple specialized sub-agents. We are particularly interested in professionals with strong hands-on experience in frameworks such as LangChain, LangGraph, Agno, or any equivalent agent orchestration framework that supports structured multi-agent workflows, tool execution, and state management.
Our main priority is not database design or infrastructure setup, but rather the core orchestration engine itself. The system must support real supervisor-to-subagent delegation (not simple swarm-style horizontal agents), proper and verifiable tool execution (no simulated tool usage), structured state handling, and dynamic integration with MCP (Model Context Protocol) servers and external tools or APIs. The architecture must be multi-tenant by design, ensuring tenant isolation and scalability, while allowing dynamic tool registration and modular extensibility.
The engine must satisfy three fundamental requirements: efficiency, low operational cost, and architectural flexibility. Efficiency means predictable orchestration, reliable task delegation, minimized hallucinations, and deterministic execution flows. Low cost implies token optimization strategies, intelligent model routing (e.g., using smaller models when appropriate), minimizing redundant calls, and clear cost observability per execution. Architectural flexibility means the system should support dynamic tool loading, MCP integration, modular agent definitions, and easy extension for new capabilities.
In addition to the core engine, we are interested in incorporating advanced features such as human-in-the-loop workflows, automated summarization, regeneration mechanisms, hybrid workflow + agent structures, structured logging, versioning of agents, observability, and potentially continuous improvement mechanisms. These features are complementary, but the absolute priority is building a solid, production-grade multi-agent orchestration engine.
We are seeking more than just implementation support—we want strategic guidance. The ideal consultant should help us evaluate and choose the right framework, share examples of similar architectures, define orchestration standards, avoid common pitfalls in multi-agent systems, and assist in structuring the foundational layer of this engine correctly from the start. Our SaaS relies heavily on AI agents to perform real tasks in production, so the system must be stable, scalable, predictable, and fully controllable in terms of cost and behavior.
Our main priority is not database design or infrastructure setup, but rather the core orchestration engine itself. The system must support real supervisor-to-subagent delegation (not simple swarm-style horizontal agents), proper and verifiable tool execution (no simulated tool usage), structured state handling, and dynamic integration with MCP (Model Context Protocol) servers and external tools or APIs. The architecture must be multi-tenant by design, ensuring tenant isolation and scalability, while allowing dynamic tool registration and modular extensibility.
The engine must satisfy three fundamental requirements: efficiency, low operational cost, and architectural flexibility. Efficiency means predictable orchestration, reliable task delegation, minimized hallucinations, and deterministic execution flows. Low cost implies token optimization strategies, intelligent model routing (e.g., using smaller models when appropriate), minimizing redundant calls, and clear cost observability per execution. Architectural flexibility means the system should support dynamic tool loading, MCP integration, modular agent definitions, and easy extension for new capabilities.
In addition to the core engine, we are interested in incorporating advanced features such as human-in-the-loop workflows, automated summarization, regeneration mechanisms, hybrid workflow + agent structures, structured logging, versioning of agents, observability, and potentially continuous improvement mechanisms. These features are complementary, but the absolute priority is building a solid, production-grade multi-agent orchestration engine.
We are seeking more than just implementation support—we want strategic guidance. The ideal consultant should help us evaluate and choose the right framework, share examples of similar architectures, define orchestration standards, avoid common pitfalls in multi-agent systems, and assist in structuring the foundational layer of this engine correctly from the start. Our SaaS relies heavily on AI agents to perform real tasks in production, so the system must be stable, scalable, predictable, and fully controllable in terms of cost and behavior.