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 bring to the table • Leadership • Hands-on technical leadership with real engineering vision. You've led delivery for a small team, ideally for 2-3 years of people leadership, as a tech lead, engineering manager, or player-coach. Formal people-management experience is useful, but the essential need is someone who can wrangle delivery and provide senior technical direction • A style that raises the bar. You give direct, specific technical feedback and hold high standards, and the team leaves those conversations more capable and more motivated • Delivery discipline. You're comfortable owning timelines, allocating work, and following projects through to the end Technical depth • 7+ years of software engineering experience, and you've shipped AI features to production and owned the lifecycle from problem framing to post-launch iteration, not just prototypes or AI-assisted coding • Strong fullstack fundamentals. Comfortable across frontend, backend, and infrastructure as needed • Practical experience with