INTO AI is a Montreal-based company specialized in building and integrating AI systems inside complex enterprise environments. We started by building our own AI SaaS platform for the hospitality industry. That is where our expertise comes from: running real AI products in production, with real users and real failure modes — not MVPs, not prototypes. We know what it takes to keep an AI system working after launch. For over a year now, we have focused on consulting: helping companies deliver their AI projects and turn them into actual value. Our systems run in our clients' own infrastructure, on their data, used by their people every day. You own how an AI system behaves and how its quality gets proven, on client consulting mandates. A client, a signed scope, a two-week cadence, and a system whose quality has to be demonstrated rather than asserted. You work with an AI Engineer who builds the system, and a Delivery Lead who carries the mandate and the client relationship. You write the sprint scope every cycle, and requirements an engineer can build from without a follow-up meeting — including the data dependencies, the latency budget and the acceptance criteria. You run sprint demos for non-technical audiences, facilitate working sessions with their subject‑matter experts, and say no to out-of-scope requests with an argument that holds. You have shipped at least one AI system into production and measured its quality after launch — whatever your title was at the time. Most candidates who fit come from an engineering background, product management, product ownership or data. You understand how LLM systems work — retrieval, structured outputs, agentic patterns, the quality/latency/cost tradeoff — and how they fail.



You will not write production code, but you read and write enough Python and SQL to automate a task, run a test and process evaluation data — with an AI coding harness doing the heavy lifting. This is why we favor an engineering degree or an equivalent foundation: if you need an engineer to test a prompt variation, the iteration loop that makes this role valuable disappears. English is required. French is a plus — some of our client work happens in French. Internal communication, documentation and meetings are always in English. That is the bar for a senior. We are also open to a strong intermediate with real potential: we have an internal training program and we invest in it seriously. On whether the system meets the bar you committed to — and whether it actually changes something for the people who use it. Like building the tooling as much as using it. We build our own: a platform for managing production prompts, an operational system carrying project state across the firm, reusable AI skills our team uses daily. Remote, and what that actually means here No relocation. Remote has been how we work from the start, not something we added recently. The time zones work, but you need to be flexible. From North Africa, that means working afternoons and early evenings; Compensation is benchmarked against your local market and set at the top of it — well above what you would earn locally. On top of base compensation, we run a bonus program tied to what you deliver. You work on cutting-edge AI projects with real impact, for clients who depend on what you ship. Internal training program, direct exposure to clients from your first mandate, and a firm small enough that what you build shapes how everyone works. #

📌 Product marketing manager manager (Montreal)
🏢 Into
📍 Montreal