AI Lead / Engineering Manager

This role is part of our Usersnap team, one of our growing brands at saas.group . Profile Overview Usersnap is building AI-driven capabilities into the core of the product, from automated setup to intelligent surveys, contextual analysis, and reporting that generates itself based on connectors. We're looking for a hands-on AI Lead / Engineering Manager to lead the small engineering team that ships these capabilities, and to set the technical direction while doing it. This is a player-coach role. You'll write and review code, make architecture calls, and define what "good" looks like for AI-native development. You'll also own team coordination, delivery timelines, workload allocation, and follow-through, so that work planned is work shipped, reliably and at production quality. This is a full-time, engineering-led role. It is not combined with product management: the technical leadership scope is substantial on its own, and you'll partner with Product rather than absorb it. AI capability is becoming core to Usersnap's product and go-to-market, especially as we move upmarket. You'll be the person who makes sure it ships well. Your immediate impact in the first 3-6 months will be: You'll have established a clear delivery rhythm for the team: shared priorities, realistic timelines, visible workload, and projects that get followed through to launch You'll have assessed the current state of the codebase, tooling, and practices, and set out a concrete engineering bar covering observability, testing, security, and architecture You'll have shipped at least one AI-powered feature to production with the team, from prototype through launch, using timeboxed AI development You'll have built a strong working relationship with Product and the rest of the company, and be seen as a reliable owner of ambiguous, technically complex work You'll have raised the team's standards while keeping people motivated and engaged Your responsibilities Team leadership and delivery Own team coordination, delivery timelines, workload allocation, and project follow-through for a small engineering team Keep work moving: break down ambiguous goals, sequence them with Product, flag slippage early, and make sure commitments are met Give clear, constructive technical feedback in code review, design discussions, and 1:1s, and raise standards without demotivating people Grow the team's capability, including how we use AI tooling to work faster without lowering quality Technical leadership Assess the current engineering bar and raise it. Define what "good" looks like for AI-native development, and make it the shared standard Set and enforce expectations in five areas: Observability: logging, monitoring, and tracing that make AI behavior, cost, and failures visible Testing: strong test coverage and automated-testing rigor, including evaluation of AI outputs Security and compliance: secure-by-default practices, as we move upmarket Scalable architecture and data pipelines: designs that hold up as usage and integrations grow Timeboxing: practical limits on AI development work, so exploration stays bounded and ships Own or steward key architecture decisions, including model selection and integration approach, and the tradeoffs between quality, cost, and latency Flag technical risks early, especially around AI reliability, cost, and edge-case behavior Hands-on contribution Design, build, and ship AI-powered features alongside the team, from prototyping through production quality Work hands-on with LLMs, embeddings, and related AI tooling to solve real product problems Build across the stack as needed. Every feature you ship should be tested, monitored, and maintainable Partnership Partner with Product on requirements and priorities, pushing back with technical reality when needed Partner with company leadership on technical planning, resourcing, and sequencing What You