About Emberline
Emberline is a two-partner investment firm that buys and builds founder-led businesses. We're operators at heart. We don't buy companies to financial-engineer them; we buy them because we believe hands-on support can make a real difference. That means embedding alongside management, building infrastructure, and doing the unglamorous work that turns good businesses into great ones.
We have three portfolio companies today and are building toward five. You'll be part of a tight core team, but your day-to-day reaches across those companies and the operators, finance leaders, and advisors who support them.
Tasks
The Role
We're looking for an Applied AI Engineer to drive AI implementation across our portfolio companies. This is a new role, and frankly, a new function. You won't find a playbook for this job because it doesn't exist yet.
Your mandate: make sure our portfolio companies are actually using AI. Not talking about it, not "exploring" it, but deploying it in ways that move the needle. That means customer service automation, developer productivity tools, sales and marketing workflows, back-office operations, and whatever else makes sense.
You'll work closely with the Managing Partner to identify opportunities, evaluate tools, and get things live. Then you'll train portco teams to own what you've built. Rinse, repeat, across every company in the portfolio.
This is 80% hands-on implementation, 20% evaluation and planning.
There's no playbook for this yet, at Emberline or anywhere else. The partners will be genuine thought partners as you build the function. The expectation is not that you arrive with everything figured out; it's that you're willing to figure it out alongside us.
This is a high agency, high opportunity seat with the ceiling set by you.
Key Responsibilities
AI Implementation
- Operational Audits: Assess each portfolio company's workflows across customer service, sales, marketing, finance, and operations, then identify where AI can reduce friction, cut costs, or improve quality.
- Tool Deployment: Configure and deploy AI tools (chatbots, copilots, automation platforms, etc.) across portco functions. You're not building from scratch; you're pulling best-in-class tools off the shelf and making them work.
- Workflow Integration: Connect AI tools to existing systems like CRMs, helpdesks, marketing platforms, and codebases. You need to understand APIs, integrations, and how data flows between systems.
- Rapid Prototyping: Test new AI applications quickly. Prove value in days or weeks, not months. Kill what doesn't work; scale what does.
Vendor & Tool Evaluation
- Landscape Monitoring: Stay current on AI tooling: what's shipping, what's vaporware, what's actually production-ready. The space moves fast; you need to move with it.
- Vendor Screening: Evaluate vendors, run trials, negotiate pricing. You're the filter between hype and reality.
- Build vs. Buy Decisions: Know when to use off-the-shelf tools, when to customize, and when to build something custom.
Enablement & Knowledge Transfer
- Team Training: Train portfolio compay teams to use and maintain the tools you deploy. The goal is handoff, not dependency.
- Documentation: Build simple, usable guides for each implementation. When you move on, the portco team should be able to run it themselves.
- Cross-Portfolio Learning: What works at one portfolio company should benefit the others. Build repeatable playbooks.
Partner Support
- Weekly Syncs: Work closely with the Managing Partner to prioritize, troubleshoot, and align on roadmap.
- Experimentation Partner: Test new tools and approaches alongside the Partner. Be the person who's already tried it when the Partner asks, "What about X?"
Who You Are
- The Translator: You've written code (or managed engineers who did), but you also understand why the sales team hates their CRM and what "good" looks like in customer service. You move fluidly between technical and business conversations.
- Reaches for the Shelf Before the IDE: You reach for existing tools before writing custom code. You've configured chatbots, played with OpenRouter APIs, and automated your own workflows just because you could. You've used many of the popular AI tools, outgrown them, and kept moving.
- First in Line for Every New Tactic: You read changelogs for fun. You signed up for every AI beta, and love stress testing a new workflow from X or whatever shipped last week. You have opinions about which tools are overhyped and which are underrated.
- Ships Before It's Perfect: You'd rather deploy something imperfect and iterate than plan forever. You're comfortable with "good enough for now" when the alternative is "perfect someday."
- At Home in Any Room: You can walk into a sales team meeting, a finance review, or an engineering standup and add value. You're not intimidated by functions outside your core expertise.
- Low Ego, High Ownership: You'll configure a chatbot in the morning and write a training doc in the afternoon. No task is beneath you if it moves the ball forward.
First 90 Days
Here's what the early months look like so you can picture Monday morning:
- Audit the portfolio. Spend time inside one target portfolio company, talking to operators, watching workflows, and understanding where friction lives. You'll come out of that with a documented view of where AI can actually move the needle, ranked by impact and effort.
- Get something live. Pick the highest-leverage opportunity and ship it. A customer service chatbot, a sales automation, a dev productivity tool: something concrete and working, not a roadmap slide.
- Build handoff documentation. Everything you deploy needs to be owned and maintainable by the portco team after you leave. That means simple, usable guides and training sessions that stick.
- Establish a vendor shortlist. Evaluate the landscape and land on a go-to stack for the use cases that keep coming up across the portfolio. You'll become the filter between AI hype and tools that actually work in the field.
- Establish the working rhythm. Build a clear cadence with the Managing Partner for prioritization, troubleshooting, and roadmap alignment. That rhythm is the operating system for everything else.
Requirements
Qualifications
- Experience in Product Management, Solutions Engineering, Technical Operations, or similar roles where you shipped things that required both technical and business judgment.
- Hands-on experience with AI/ML tools in a professional context, whether that's deploying chatbots, integrating LLM APIs, building automation workflows, or similar. We care about what you've actually done, not credentials.
- Strong understanding of APIs, integrations, and how systems connect. You don't need to be a software engineer, but you need to be able to read API docs and understand what's possible.
- Excellent communication skills. You'll be explaining technical concepts to non-technical operators and translating business needs into technical requirements.
- Comfort with ambiguity. This role doesn't have a playbook. You'll figure it out as you go.
- This is a remote role but on-site engagements, rapport building and investigations are a key requirement, which will require travel.