IgniteTech
AI-DNA Solutions Engineer
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Build the System. Let AI Do the Work. Own the Result.
We're looking for a Technical Enablement Engineer who operates AI-first by default — someone who builds the prompts, agent workflows, and knowledge systems that let AI own enablement at enterprise scale, then verifies and ships what comes out. You'll be the person Fortune 100 brands meet during onboarding, demos, and training — backed by AI agents you design and direct.
This isn't a traditional enablement seat. There's no hand-writing docs, no recycling slide decks, no sitting in a loop with a chatbot. You architect the system; the system does the production. One person plus their agents, delivering what used to take a full team.
What You'll Do
- Onboard enterprise customers onto community engagement and social media management products — running structured sessions that get users to value fast, with each cohort benefiting from a smarter, faster system you continuously refine.
- Deliver and prepare product demos for clients, prospects, and executive audiences alongside product leadership — including demo environments, storylines, dry runs, and real-time coverage of new features.
- Own documentation and training across the product family, keeping everything current within days of each release using AI agents and verification workflows you build yourself.
- Design and improve the AI-native system at the core of the role — the prompts, skills, context, knowledge bases, and feedback loops that let agents autonomously produce enablement work with less human intervention over time.
- Turn customer signals into leverage — recurring questions become self-serve resources, onboarding friction becomes structured product feedback, and every improvement is shared so the whole team accelerates.
- Maintain demo-readiness — environments, data sets, talk tracks, and storylines kept sharp across the full product family.
- Deliver structured async updates — daily check-ins, work logs, and weekly goal reports with demos.
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What We're Looking For
- AI-native operator. You don't treat AI as a side tool — you build the context, examples, and guardrails that let agents own work end-to-end. You direct, verify, and get out of the way.
- 3+ years in enterprise community engagement or social media management platforms — hands-on with managing, administering, onboarding, or enabling users on these products.
- Proven customer-facing chops. You've run live demos, trainings, or onboarding for enterprise buyers. You're clear, confident, and credible in front of a Fortune 100 room — composed next to an SVP.
- Closed-loop quality discipline. You verify every AI-produced deliverable against the live product. No hallucinated steps, no stale screenshots, no "good enough."
- Strong product sense and writing quality. Product leadership can hand you a goal — not a spec — and trust what you deliver will be polished and audience-appropriate.
- Frontier AI fluency. You delegate real work to agents, you're never locked to one tool, and you have sharp, current judgment on which model fits which task.
- Ownership mindset. Mission-driven, self-directed, proactive on the boring stuff too. You treat blockers as puzzles, not escalation tickets.
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Bonus Points
- Admin-level experience running an enterprise community or social platform instance.
- Background in instructional design, technical writing, or curriculum development.
- Video production skills for walkthroughs and training content.
- Prior solutions engineering or sales engineering experience.
- Hands-on building AI assistants, chatbots over knowledge bases, or contributing to shared prompt/skill libraries.
Why This Role Compounds
You'll sit at the intersection of enterprise enablement, applied AI systems, and customer success for major brands. The real growth here is in the skill that matters most right now: building durable leverage — prompts, agent workflows, knowledge systems — that lets AI own more of the work over time, and sharing it so an entire team gets faster.
The AI-native way of working is already in production here. No token limits. No tooling restrictions. If the right answer is a better model, a new agent config, or a tool that isn't in the stack yet, that conversation is always open. You'll help define what "forward deployed" looks like in the AI era.
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