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# Senior Software Engineer – AI

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/saas-group.md)Share20 Sep 2026Published32Listing views2Application actions20 Oct 2026Apply before  Opportunity details

## About this role.

AI SummaryUsersnap is seeking a senior individual-contributor software engineer to design, build, and operate AI-powered product features end to end. The role centers on practical production use of LLMs, embeddings, RAG, evaluation methods, vector databases, and multiple model providers. The engineer will make architecture and model-integration decisions while balancing quality, cost, latency, reliability, and maintainability. This is a highly autonomous full-stack role that partners closely with engineering leadership and product in a lean, remote European team.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

4/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

4/5IndependentCollaborative

AI insightThis is a senior production AI engineering role requiring 7+ years of software experience plus demonstrated ownership of live LLM-enabled features. The engineer must independently resolve ambiguous technical tradeoffs across application development, data, infrastructure, evaluation, and operational reliability.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianHighly competitive$215,000US market range$180k–$250k0$275k

AI insightNo actual compensation amount is disclosed; the company only states that location-adjusted salary ranges will be provided before the first interview. The figures shown are estimated US annual base-salary market benchmarks in USD for a senior full-stack AI/software engineer with production LLM and technical-ownership experience; actual pay may differ substantially because this role is remote and Europe-based with location-adjusted compensation.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[LLM integration](https://jobicy.com/jobs?search_keywords=LLM%20integration.md)[Full-stack development](https://jobicy.com/jobs?search_keywords=Full-stack%20development.md)[RAG](https://jobicy.com/jobs?search_keywords=RAG.md)[Prompt engineering](https://jobicy.com/jobs?search_keywords=Prompt%20engineering.md)[Embeddings](https://jobicy.com/jobs?search_keywords=Embeddings.md)[Vector databases](https://jobicy.com/jobs?search_keywords=Vector%20databases.md)[AI evaluation](https://jobicy.com/jobs?search_keywords=AI%20evaluation.md)[SQL and data pipelines](https://jobicy.com/jobs?search_keywords=SQL%20and%20data%20pipelines.md)[Docker and CI/CD](https://jobicy.com/jobs?search_keywords=Docker%20and%20CICD.md)[Observability and monitoring](https://jobicy.com/jobs?search_keywords=Observability%20and%20monitoring.md)

Sample interview questionsDescribe an AI-powered feature you delivered from initial concept through production. How did you define success?I would explain the user problem, why an AI approach was appropriate, the prototype and architecture selected, and the launch plan. I would also describe measurable success criteria such as task completion, answer quality, latency, cost per request, adoption, and error rates, then show how post-launch findings informed iteration.

How would you decide whether to use RAG, fine-tuning, prompt engineering, or a non-AI solution for a product requirement?

I would start with the task, data characteristics, quality threshold, operational constraints, and expected user value. Prompting is often the fastest baseline; RAG fits when current or proprietary knowledge must be grounded, while fine-tuning is justified only when repeated behavior gaps and sufficient high-quality data support its cost. If deterministic logic meets the need more reliably and cheaply, I would recommend that instead.

What practices would you use to make an LLM feature reliable in production?

I would implement versioned prompts and models, representative offline evaluation sets, structured outputs where possible, guardrails and fallbacks, logging with privacy controls, monitoring for quality, latency, and cost, and staged rollout experiments. I would also define escalation paths for unsafe, low-confidence, or failed outputs.

How do you balance AI response quality, latency, and operating cost?

I would establish target metrics and measure tradeoffs through controlled experiments. Typical tactics include model routing, retrieval optimization, caching, token-budget controls, asynchronous workflows where appropriate, and using smaller models for simpler tasks while reserving stronger models for high-value or complex requests.

How would you work with Product when a requested AI capability is ambiguous or technically risky?

I would clarify the customer outcome and failure tolerance, identify assumptions and risks, and propose a small testable scope with explicit success metrics. I would communicate constraints transparently, present alternatives with their tradeoffs, and use prototype or evaluation evidence to guide the product decision rather than treating the initial request as fixed.

This role is part of our [Usersnap](https://usersnap.com/) 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 Senior AI engineer who designs and ships AI-powered features end-to-end, working closely with fellow engineers to bring these capabilities to production quality.

This is an IC role with real technical ownership. You’ll be trusted to make architecture decisions within your area, not just execute someone else’s spec, and to spot problems and propose solutions before anyone has to ask. AI capability is becoming core to Usersnap’s product and go-to-market, and you’ll be one of two engineers making sure it actually ships reliably and at production quality.

### Your immediate impact in the first 3-6 months will be:

* You’ll have shipped at least one AI-powered feature to production, end-to-end, from prototype through launch
* You’ll have established technical patterns for AI feature development that the rest of the team can reuse
* You’ll have built a strong working rhythm with the rest of the Engineering Team and Product
* You’ll be recognized as a reliable owner of ambiguous, technically complex work

### Your responsibilities

* Design, build, and ship AI-powered features across the product, from prototyping through production quality
* Work hands-on with LLMs, embeddings, and related AI tooling to solve real product problems, not just experiment with them
* Own technical decisions for your features: architecture, model selection and integration approach, and the tradeoffs between quality, cost, and latency
* Build across the stack as needed. This is a fullstack AI dev role, not siloed to backend or frontend, and every feature you ship should be tested, monitored, and maintainable
* Help establish good practices for AI feature development as our AI surface area grows, and flag technical risks early, especially around AI reliability, cost, and edge-case behavior
* Partner closely with the Senior Engineering Manager on technical planning and sequencing, with Product on requirements (pushing back with technical reality when needed), and with the rest of the engineering team on shared architecture and standards

### What You bring to the table

* 7+ years of software engineering experience, 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
* Evaluation mindset, you define success metrics, design experiments, run offline and online evaluations, and make decisions from the results
* Data competence, solid SQL, basic schema design, the ability to build simple pipelines, and a willingness to get your hands dirty with data through analysis and exploration
* Production readiness, Docker fluency, CI/CD awareness, logging and monitoring basics, 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
* A track record of proactive problem framing, identifying problems and proposing solutions on your own, rather than waiting to be told exactly what to build
* Compliance or security experience, or a strong interest in it, is a genuine plus as we move upmarket
* Comfortable in a lean team where you own outcomes, not just tickets

### What’s in it for You

* Ultimate flexibility: We’re 100% remote. You can work from wherever you like, whenever you like.
* Freedom and autonomy: We’re a high-trust team, and you’ll be given lots of flexibility to solve problems in your own way — with plenty of help from the team when you need it.
* Minimum bureaucracy: We don’t like to get bogged down with meetings and red tape. We like to be efficient and keep momentum steady & sustainable.
* Small & friendly team: We help each other out, have fun, and joke around.
* Our network: We are a community of entrepreneurial SaaS professionals that regularly exchange ideas, knowledge, learning and expertise with each other internally.
* Flexible time off: We want you to recharge your batteries when needed.

[Usersnap](https://usersnap.com/) is part of saas.group and we have a shared goal of succeeding together.

### What is saas.group?

Think of us as the driving catalyst behind your favorite software success stories. saas.group, established in 2017, is on a mission to turbocharge promising B2B SaaS ventures, unlocking their full potential. As a Software-as-a-Service portfolio powerhouse, we specialize in acquiring small software treasures and polishing them into industry stars. With a dynamic, fully remote team of nearly 380+ colleagues spanning 50+ countries we are truly global and we are rewriting the playbook on SaaS innovation and collaboration. We grow [25 exciting brands](https://saas.group/family/) under our umbrella. Feel free to explore our [Candidate’s Hub](https://saas.group/candidate-hub/) to get an insider’s view, dive into our culture, gain valuable insights into our teams and how we work.

Also, don’t forget to follow [saas.group on LinkedIn](https://www.linkedin.com/company/saas-group/) to stay up-to-date on our job openings, podcast episodes, and with all things happening at our company.

We don’t just simply offer you to “take a job with us” but rather to “join us on this journey” to build the world’s largest platform of independent SaaS companies.

Sounds like something you’re up for? Feel free to apply even if you don’t check all these boxes. We believe in the power of diversity and fresh perspectives to spice up our team. Your application might just be the missing ingredient!

—

saas.group is committed to creating a diverse and inclusive workplace. We are proud to be an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. We celebrate diversity and are committed to creating an inclusive environment for all employees. If you’re passionate about working with a team that values innovation, excellence, and fairness, we encourage you to apply.

We believe in fair and transparent pay. Salary ranges at saas.group are location-adjusted, targeting local market medians for a remote-first environment, and will be shared with our potential future employees in writing before your first interview – so you have everything you need to evaluate the opportunity.

Show more

[Apply now >](https://jobicy.com/jobs/153695-senior-software-engineer-ai.md)

>  Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md)

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