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-

…