AI Forward Deployed Engineer

What You'll Do

• Partner with a business counterpart to identify and scope operational bottlenecks, then build the tools to fix them
• Move autonomously across the organization: gather requirements, secure data access, and earn the trust of teams you're helping
• Ship quickly using whatever fits: n8n, Make, Zapier, Lovable, or custom Python. Right tool for the right job.
• Build and maintain production AI systems: RAG pipelines, conversational interfaces, workflow automation, internal agents
• Run workshops and training sessions to help non-technical teams actually use what you build
• Define metrics upfront, track them, and tell the story of what changed

What We're Looking For

We care more about what you've shipped than where you worked. Show us:

• A portfolio of things you've built. Personal projects, side tools, public demos, anything you can point at. GitHub, a writeup, a Loom walkthrough, a live URL. All count.
• Evidence you move fast with LLMs. You've run multiple coding agents in parallel and know how to coordinate between them. You have a feel for how capable frontier models actually are, and you match model intelligence to task complexity rather than defaulting to the biggest one every time.
• Production instincts. You've dealt with the gap between "it works in the demo" and "it works at 3am when nobody's watching." Evals, error handling, observability. You've thought about this.
• A process-driven mindset. You document how things work before someone asks you to. Your SOPs and checklists aren't overhead — they're how you stay sharp and move fast without dropping things.
• You've always had ideas. Now you can ship them. AI coding agents removed the bottleneck that used to sit between your thinking and working software. You don't wait for an engineer to build what you've already figured out.
• Communication that closes the gap. You can translate what you built into terms that matter to the business partner who asked for it.
• Formal experience: 2+ years in a role where you shipped tools that people actually used.

Reports to:

Product Manager - Exchange Product

AI Engineer — Internal Tools & Automation

The AI Engineering team acts as an internal catalyst. We identify where the business is slow, build tools to fix it, and move on. Our work spans Customer Success, Exchange Operations, Trading, and Custody — wherever there's friction worth eliminating.

This isn't a research role. We ship fast, measure impact, and iterate. You'll spend more time prototyping with LLMs and building agentic workflows than writing infrastructure, but you'll own what you build all the way to production.

Nice to Haves

• Experience with vector databases (Qdrant, Pinecone, pgvector)
• Familiarity with SQL and relational databases so you can pull your own data
• Docker / containerization basics
• Any exposure to blockchain or trading concepts (we'll teach you what you need)

As part of your application, please submit a short video introduction (5–10 minutes). Screen-share and walk us through an AI project you've built: what problem it solved, the decisions you made, and how it holds up in production.

Please record the video on your device and share an accessible Google Drive link containing the file (in MP4, WMV, or MOV format) within your submission. A Bullish talent acquisition team member will reach out to request access upon review.

Bullish is proud to be an equal opportunity employer. We are fast evolving and striving towards being a globally-diverse community. With integrity at our core, our success is driven by a talented team of individuals and the different perspectives they are encouraged to bring to work every day.