Chicago Atlantic
Technical Product Manager / Forward Deployed Engineer — AI & Finance
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Location: to be based in the UK or Europe (Netherlands, France, Germany, Spain, Italy), interfacing with AI teams in Vietnam and stakeholders in the US.
Type: Full-time
Reporting line: Head of AI Platforms
About Us
We're an AI-first builder giving underdog companies a new lease of life with AI. We are launched out of Chicago Atlantic, a $3B AUM private credit/debt firm with a top tier fund performance record and deep expertise in AI roll-ups and restructurings. We've spent years inside the deals, and we've seen exactly how much value AI can create for smaller companies that lack the capabilities to compete with larger companies. Now we're building the AI systems to change that: an AI-obsessed team applying frontier AI engineering to the workflows that finance, banking, and restructuring actually run on.
We're small (a ringfenced AI team of 35 and growing), we ship, and we sit close to real capital and real operators. That's the difference between AI that demos well and AI that changes how the work gets done.
What We Are Building
Restructuring is one of the highest-stakes, most knowledge-intensive corners of finance, and one of the least touched by modern tooling. We know applied AI works here because we've already done it ourselves: we turnaround underperforming assets from $100M to >$1.1B in annual revenue, applying AI to operations and seeing the gains firsthand, giving capabilities that level the playing field for an ecosystem of smaller companies.
Now we're pointing that capability at restructuring itself, building the product layer for the work that matters most: diagnosing distressed businesses, valuing assets at scale, and surfacing where value can be unlocked with AI. We've proven the core, and the product itself is ready to grow. If you want to help decide what gets built in a domain with huge upside, this is the place.
Why This Role Is For You
You've done technology inside a finance or consulting environment, and you know how hard it is to launch AI products in a fast-paced professional services setting. You think like a product builder, not just an engineer: you care about what gets used, not just what gets shipped. You want to be on the side that fixes it, building a solution that works with the domain knowledge users already have.
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You'll work at the forefront of the stack: harness engineering and plugin-based solutioning, systems deployed against live workflows. You'll have the freedom to define what gets built and the proximity to clients to see your work land in weeks, owning the product decisions as much as the technical ones.
You are looking for ownership, hard problems, and immediate, visible impact. You'll join a nascent team alongside an existing Technical Product Manager, dividing and conquering problems across the entire restructuring process.
Core Responsibilities
- Lead a team of AI engineers — set direction, unblock execution, grow technical talent, and own team output
- Deploy on the ground — embed with US clients (knowledge workers, operators, finance teams) to diagnose workflows, map real user needs, and translate findings into working systems
- Build 0-to-1 — own the full lifecycle from discovery to shipped product; define what gets built, why, and how it gets measured
- Orchestrate AI systems — design and manage multi-agent workflows and platforms using LLMs; work alongside AI engineers to ship production-grade AI solutions
- Lead professional services transformation — apply AI to the high-value knowledge work that firms run on, from analysis and reporting to restructuring and portfolio monitoring; you'll learn the domain well enough to challenge and redesign it
- Own the product — drive initiative without being directed; set goals, prioritise ruthlessly, and hold the outcome — not just the delivery
- Translate between worlds — speak the language of the business on one side and engineers on the other; you're the connective tissue that stops both sides talking past each other
Must-haves
- Forward-deployed experience — you've worked directly inside client environments, not just taken requirements and handed off; you were in a technical role (engineering, data, or systems design) before moving to product
- Knowledge work and professional services experience — you've worked in or alongside professional services, and you understand the kind of complex knowledge work these firms run on
- AI agent and platform management — hands-on experience running AI agents in production: prompt engineering, agent orchestration, platform reliability, and knowing when to rebuild vs. patch
- People leadership — you've led or directly guided technical talent
- 0-to-1 builder track record — you've taken something from ambiguity to working product; you don't wait for requirements to be handed to you
- Strong independent judgment — you operate without hand-holding; you surface blockers, make calls, and move fast in ambiguous environments
- Finance domain knowledge — working understanding of FP&A, financial modelling, or restructuring; useful but not required, you can pick up the domain on the job
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Nice-to-haves
- Experience in management consulting, investment banking, or finance ops in a startup
- Familiarity with AI agent frameworks
- Comfortable working across US time zones and able to travel to the US as needed
What We Offer
- Direct impact — you'll own meaningful products at a company small enough that your decisions visibly matter
- AI-native environment — work with a team of AI and data engineers who move fast and build seriously
- Flexible work — work across our hubs: Europe, US and Vietnam
- Growth — as we scale, this role grows with us; early team members shape what the company becomes
- Compensation — Base salary + equity + benefits; we'll be upfront about the full package on the first call
Hiring Process
- Apply — send your CV and two sentences on the most complex system you've built or deployed
- Intro call — we cover your background and the role; you decide if it's worth going deeper
- Interview — a real problem we've faced and how you might help solve
- Fit/teamwork interview — meet alongside the CDO and understand the team culture
- Final conversation — meet the team, ask questions
- Offer
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