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:

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• 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 LLM APIs, prompt engineering, RAG, or similar techniques in live systems, with good judgment about when AI is the right tool and when it isn't

• Observability and testing rigor. You build in logging, monitoring, and tracing from the start, and you insist on strong test coverage and automated testing, including evaluation of AI behavior

• Scalable architecture and data pipelines. Solid SQL, schema design, and the ability to design pipelines and systems that scale

• Evaluation mindset. You define success metrics, design experiments, run offline and online evaluations, and make decisions from the results

• Production readiness. Docker fluency, CI/CD, and good habits for versioning prompts, models, and datasets

• LLM platform breadth. Experience integrating multiple LLM providers, working with vector databases, and using LangChain (or similar); comfortable fine-tuning when it truly makes sense

• Security and compliance experience, or a strong interest in it, as we move upmarket

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