AI Senior Software Engineer in Test

Software Engineer in Test – Drive Innovation with AI & CI/CD Excellence in SaaS

Join Infiterra and help power the future of subscription e-commerce.

Infiterra’s B2B SaaS platform helps IT Distributors and Managed Service Providers (MSPs) automate and grow their subscription business. With 100+ customers in 75 countries, we're recognized for innovation and global impact — and we’re just getting started. We foster a collaborative and growth-oriented culture, allowing you to be part of a dynamic, forward-thinking team.

The role in a nutshell

As a Software Engineer in Test, you’ll be at the heart of delivering quality at scale. Your mission is to make our testing smarter, faster, and more resilient — from evolving our automation frameworks to embedding AI-powered quality checks into the development lifecycle. You’ll ensure our platform is robust, secure, and always ready for global customers by building tests that catch issues early and by shaping quality practices across teams.

Our Ideal Candidate Profile

You’re not just writing tests — you’re shaping how quality is built into software. You thrive on automation, but you also know when to apply AI tools to eliminate repetitive work and uncover insights humans might miss. You enjoy collaborating with developers and product teams, moving testing left, and making sure quality never becomes an afterthought.

You’re curious, hands-on, and outcome-driven: reducing flakiness, speeding up releases, and strengthening customer trust are what success looks like for you.

Your Role & Responsibilities

Test Automation & Framework Evolution

• Build and enhance automation frameworks with C#, Selenium, Reqnroll (SpecFlow), REST clients, and Playwright.
• Shift focus from UI-heavy testing to scalable, integration-first automation.
• Collaborate with developers to ensure maintainability, isolation, and a clean, decoupled test codebase.

AI-Powered Quality Engineering

• Apply AI tools (copilots, LLMs, self-healing locators, anomaly detection) to boost testing efficiency and reduce maintenance.
• Auto-generate and review UI, API, and end-to-end tests; summarize failures; detect flaky tests and regressions.
• Design performance, resilience, and security tests using k6, Gatling, OWASP ZAP, and Burp Suite.
• Drive proof-of-concepts and adoption of new AI/ML-driven quality practices.

CI/CD & DevOps Alignment

• Integrate automated tests into Azure DevOps pipelines with strong quality gates.
• Monitor pipeline health, prevent flaky builds, and ensure continuous delivery confidence.

Defect Prevention & Test Advocacy

• Collaborate with business analysts and developers to ensure clear, testable requirements.
• Identify complexity early and advocate for pragmatic, automation-friendly solutions.

Continuous Improvement & Ownership

• Track and improve quality metrics (coverage, flakiness, escaped defects).
• Suggest optimizations in test strategies, test data, and pyramid balance.
• Stay ahead of trends in test tooling, AI, and modern quality engineering practices.

Requirements

What You Bring

• 3+ years in test automation with C#, Selenium, Reqnroll (SpecFlow), and REST APIs.
• Strong knowledge of BDD, Gherkin syntax, and writing clear, maintainable feature files.
• Experience integrating automated tests into Azure DevOps pipelines or similar CI/CD workflows.
• Background in large-scale SaaS platforms and service-oriented/modular architectures.
• Strong collaboration skills with developers, POs, and BAs to clarify requirements and deliver quality.
• Hands-on experience with AI copilots/tools (e.g., ChatGPT, GitHub Copilot) for test generation, UAC parsing, or diagnostics; foundational understanding of LLMs/ML frameworks (OpenAI API, Hugging Face, TensorFlow) and prompt engineering.
• Solid grasp of performance testing practices and familiarity with observability tools (logs, metrics, traces).
• Skilled in contract testing, test data management, and mocking/stubbing strategies (e.g., Moq, NSubstitute).
• Analytical problem-solver with experience in root-cause analysis of test failures, flaky pipelines, and system issues.
• Ability to proactively identify architectural or business logic constraints that hinder scalable testing.

Bonus Points

• Experience with self-healing or AI-assisted test frameworks.
• Familiarity with data-centric testing and synthetic data generation.
• Exposure to advanced AI tools or frameworks (e.