Expert AI Developer for Multi-Agent Environment and VM Setup

๐ŸŒ Remote, USA ๐ŸŽฏ Full-time ๐Ÿ• Posted Recently

Job Description

I need help putting together a user friendly multi-agent environment set-up for my own personal use to do: a) agentic programming to build software, project development and deployment, and b) research. Iโ€™ll almost exclusively be working on software development, tooling, and apps.

There is a ton of information online from people using different multi-agent workflows, IDEs, and platforms and it is a bit confusing figuring out what to use. Let me know if you have an account on x.com so I can share links to some posts of ideas that look interesting.

I want to work with someone who has extensive, daily experience using agents for multi-orchestrated project development. This person should also have extensively tested different agents and automation tools:

  • Running multi-agent teams in Claude Code successfully, using tools like โ€˜Superpowersโ€™ and โ€˜Skillsโ€™
  • Using multiple LLMโ€™s in a workflow so not to depend on one model (Opus, Gemini, GPT collaborating)
  • Have tried Openclaw (and variations of it like Ironclaw that provide greater security) that are fully-open where there is more customization and potential to run locally; also platforms like Perplexity Computer that are closed but easy to use

I want to run the set-up on a VM so that I have full control of the server and its private, I have access to the top GPUs, tasks can run 24/7, resources can scale if/when necessary, I can get notifications on my phone, and I could move everything local if I wanted.

Here is an example of a work flow that looks interesting to me:

Real-time search โ†’ Grok 4.20

Planning โ†’ Opus 4.6

Coding (complex) โ†’ Claude Code (Opus 4.6)

Coding (well-defined) โ†’ GPT-5.3 Codex X High

Write Tests โ†’ Gemini 3.1 Pro

Run Tests โ†’ GPT-5.3 Codex

Debug โ†’ Opus 4.6 (1M)

Some technical guardrails required:

1. Implement a checkpointing system for long-running swarms (orchestrator) to ensure no memory is lost during multi-hour processes.

2. Token loop prevention: A credit monitor to prevent recursive loops from exceeding a budget.

Required Skills:

Multi-Agent Orchestration: Proven experience setting up Claude Code with custom tools and skills.

AI Model Integration: Expertise in configuring multi-model workflows involving Anthropic (Opus), Google (Gemini), and OpenAI (GPT).

Linux Server Administration: Advanced knowledge of VM management, driver installation (CUDA/NVIDIA), and server-side security.

Open Source Agent Frameworks: Daily experience with open-source tools like Openclaw, Ironclaw, or similar customizable platforms.

Network Security: Ability to configure firewalls and egress filtering to prevent unauthorized data transfer (data exfiltration).

Python/Scripting: Proficiency in writing the automation scripts necessary to connect different agents and manage task handoffs.

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