Remote AI Automation & AI Agent Engineer

What I deliver remotely

Remote AI engineering works when scope, interfaces, evidence, and acceptance criteria are explicit. I start with the business workflow, identify the systems involved, map risky actions, and define the smallest end-to-end result worth shipping.

Engagement sequence

1. Discover: identify the bottleneck, users, tools, data, and required outcome.
2. Blueprint: map triggers, decisions, tools, memory, approvals, and failure paths.
3. Build: implement the agent, automation, integration, or supporting website.
4. Test: run realistic scenarios, including failures and edge cases.
5. Deploy and hand over: document operation, ownership, and the next improvement loop.

Best-fit remote projects

• AI agents connected to messaging, email, CRM, calendars, and internal tools
• Workflow automation with model-assisted classification or drafting
• Self-hosted and container-isolated agent infrastructure
• OpenClaw and Hermes-Agent implementation
• Multi-agent research, review, and delivery loops
• Lead systems, outreach, reporting, and API integrations

Evidence before promises

I do not publish invented rankings, anonymous testimonials, or unsupported performance claims. The public ClawBridge Codex repository, technical case study, workflow diagrams, and architecture articles provide evidence that prospective clients can inspect directly.