AI Engineer - Founding Team
Company: Dynamix Group
Location: Atlanta, GA (Remote)
Type: Full-time
Remote: Yes
Posted: 2026-05-06
About this role
Who we are
Dynamix is a 30-year-old, $300M value-added reseller reinventing itself for the AI era. We are standing up a new AI practice — a consulting subsidiary that will sell AI services and solutions to our enterprise customers. You will be one of the first two engineers we hire, reporting directly to the EVP, with a ringside seat to how a profitable, established business bolts AI into its core.
What you will actually do
- Build and ship AI automations that remove toil from Dynamix operations — procurement, ticketing, finance, proposal generation, sales enablement.
- Build customer-facing AI tools in the vein of our Order Tracking plugin — things our customers feel, not just things we feel.
- Work end-to-end: problem framing with business owners, prototype in a week, iterate to production, measure the lift.
- Help shape our offering architecture for customer engagements (Assess / Design / Deploy+Train / Maintain) as we move from internal wins to external services.
- Present your work. Every week. To the EVP, to business owners, eventually to customers.
What we need from you
- 0-2 years professional experience. CS/Engineering degree from a top program, or any 4-year STEM degree with equivalent chops.
- At least one real project shipped using an LLM API. Not a tutorial — something you designed, built, and can demo.
- Strong Python. Comfortable picking up TypeScript / JavaScript for UI and plugin work.
- Workflow automation fluency — n8n, Zapier, Make, or code-first equivalents.
- Clear written and spoken communication. You can walk a non-technical business owner through your build without making them feel dumb.
- Bias to ship. Ownership. Coachable.
Bonus
- You have a public portfolio — GitHub, X/Twitter, a blog, hackathon wins.
- You have worked a customer-facing job before (support, consulting, sales engineering, retail) — you have range with people.
- You have opinions about evals, prompt engineering, and when agents are the wrong answer.
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