# AI Lead / Engineering Manager at Saas Group > Remote · Europe · Staff · Engineering management · open. Source: https://alderhq.app/jobs/greenhouse%3Asaasgroup%3A4996386101 (read 2026-11-14). ## Facts (observed) - Company: [Saas Group](https://alderhq.app/companies/saas-group) - Location text: Remote, Europe; Usersnap - Where it can be done from (inferred): Remote · Europe (evidence: "Location: Remote, Europe; Usersnap") - Posted: 2026-10-02 (43 days ago) - First seen by alder: 2026-11-07 · last verified: 2026-11-07 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://job-boards.eu.greenhouse.io/saasgroup/jobs/4996386101 - Skills listed (inferred): LLMs ## Pay and sponsorship - Posted pay: not posted ## Description (the employer's text) 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Asaasgroup%3A4996386101 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.