AI Solution Engineer Lead

If you are here, it is because you know that we are looking for an AI Solutions Engineer Lead . Since 2016 we have been using AI to transform the way large companies talk with their customers. The best stories start with great conversations: the relationships that matter are built on the ability to communicate, listen and understand each other. Between companies and people, though, this kind of relationship is still rare, and we work to bring the magic back into interactions, at scale. Through our AI Agents platform we have automated millions of conversations across voice, chat and messaging, integrating with our clients’ CCaaS and enterprise systems and improving their operational and commercial performance. We are a fast-growing European scale-up, backed by Azimut (€15M investment). What are we looking for? We are looking for an AI Solutions Engineer Lead: the person who takes AI Agents from design to production with our most complex clients, defines how this craft is done at indigo.ai, and leads the small team doing it (3–4 people today, and growing). You will start on our most complex enterprise engagement: a multi-country program spanning Europe and Latin America, the proving ground where the method you define gets tested with real users and real traffic, and the natural base from which to grow our wider AI implementation organization. This is a horizontal, multidisciplinary role sitting between technology and business, not a pure software developer position. You remain the reference architect on the projects where the risk is high and the solution does not exist yet. The core of the role is functional, architectural and experiential: your value is imagining how an assistant should work (the flows, the integrations, the behavior) and making it actually work the way you imagined it. Your leverage is not typing production code line by line: the deepest technical execution is handled by Product and by the AI tools you use every day. What remains yours, and cannot be delegated, is judgment: how to frame the problem, where it can break, how to make it reliable. And what sets this role apart is that you also need to know how to transfer that judgment to your team. This is a position with real room to grow: we are scaling our AI implementation organization all over the world, and the scope of this role grows with it: more projects, a larger team, new geographies.

Key Responsibilities

• Design the agentic architecture. You decide how to frame the problem: one agent routing to specialized agents? Where are gates and guardrails needed (user identification, queue management, handover to a human operator)? You design the structure before building it, across chat and voice, which follow the same logic.
• Define the standard, not just the solution. Your architectural choices become the team’s default: playbooks, quality criteria, reusable patterns. What you solve once should not be reinvented on the next project.
• Control hallucinations, and turn it into a method. You define the boundaries of every agent: when it answers, when it retrieves information, when it must say “I don’t know” instead of inventing a plausible answer. This is where an assistant’s reliability is decided, and you establish this discipline, evaluation sets and regression tests included, for everyone.
• Translate business into behavior. You turn a business goal into concrete agent behavior: “cut queue times” or “never mention internal details to the user” become precise rules, flows and guardrails.
• Own the user experience. You design tone, messages and per-channel adaptation (voice, chat, messaging) from the point of view of the person living the interaction: perceived quality is part of the solution.
• Direct execution with AI. You use agentic coding tools (e.g. Claude Code) to build, and you read and validate what they produce. The craft is framing the problem so the machine executes it well, and knowing how to judge when the output is right and when it is not. And you teach the team to do the same.
• Ship to production and iterate. Solutions go live: you monitor performance and KPIs, manage edge cases, control the quality and reliability of the answers and iterate after go-live. The outcome is your responsibility, on your projects and on the team’s.
• Grow people. You onboard and mentor your reports, set up 1:1s and growth plans, and take part in selecting the next hires. You define what “great” means in this craft and how it is measured.
• Guide the client through decisions. You are the technical point of reference in front of enterprise decision-makers: you help the client make the right choices, give the verdict on feasibility and scoping including in pre-sales, and defend the solution when timelines come under pressure.
• Coordinate the right people. You work closely with Product, DevOps, Customer Succe