AI Lead / Engineering Manager

saas.group; United States; Remote; USD 140,000 - 200,000; Full time

Job summary

Usersnap is seeking an AI Lead / Engineering Manager to guide a small engineering team, set technical direction for AI-native capabilities, and own delivery quality. You’ll code, review, and make architecture decisions, while coordinating work with Product to ensure features are shipped on time.

This hands-on leadership role emphasizes observability, testing, security, and scalable data pipelines, with ownership of AI-powered product growth.

Qualifications

• 7+ years of software engineering experience and a track record shipping AI features to production.
• Leadership experience including delivery of a small team (2–3 years).
• Strong fullstack fundamentals across frontend, backend, and infrastructure.

Responsibilities

• Own team coordination, delivery timelines, workload allocation, and project follow-through for a small engineering team.
• Define and uphold engineering standards including observability, testing, security, and architecture for AI-native development.
• Design, build, and ship AI-powered features end-to-end from prototype to production quality.
• Partner with Product and company leadership on requirements, priorities, and technical planning.

Skills

Hands-on leadership

Delivery discipline

Fullstack fundamentals

LLM APIs & prompt engineering

Observability & testing

Docker & CI/CD

LangChain & vector databases

Tools

Docker

LangChain

CI/CD

Vector databases

Job description

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 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