Software QA Engineer (AIR Lab)

Thales Avionics, Inc. (AGS); Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

Thales Avionics, Inc. (AGS) in Singapore seeks a Software QA Engineer to drive automated integration, sanity and regression tests for AIR Lab applications.

You will balance confidence and delivery speed with the Validation Lead and Product Owners, using AI-assisted testing to accelerate coverage. You will design, implement and maintain test suites, align with product maturity, and contribute reusable testing prompts and guidelines for the wider team while ensuring high quality across distributed

Qualifications

• 4–6 years of experience in software QA or test automation.
• Solid understanding of test pyramid and risk-based testing.
• Hands-on API and service-level test automation and at least one programming language.

Responsibilities

• Design and implement automated integration tests that verify contracts and data exchanges.
• Build and maintain sanity and regression suites to protect deployment quality.
• Integrate tests into GitLab CI/CD pipelines and containerised environments.
• Collaborate with Product Owners and Validation Lead to balance risk and delivery speed.

Skills

API test automation

CI/CD

Python

Java

Docker

Education

Bachelor's degree in Computer Science/IT

Master's degree (nice to have)

Tools

Karate

Pytest

REST Assured

Postman/Newman

Job description

Location: Singapore, Singapore

Thales is a global technology leader trusted by governments, institutions, and enterprises to tackle their most demanding challenges. From quantum applications and artificial intelligence to cybersecurity and 6G innovation, our solutions empower critical decisions rooted in human intelligence. Operating at the forefront of aerospace and space, cybersecurity and digital identity, we're driven by a mission to build a future we can all trust.

In Singapore, Thales has been a trusted partner since 1973, originally focused on aerospace activities in the Asia-Pacific region. With 2,000 employees across three local sites, we deliver cutting-edge solutions across aerospace (including air traffic management), defence and security, and digital identity and cybersecurity sectors. Together, we're shaping the future by enabling customers to make pivotal decisions that safeguard communities and power progress.

The Software QA Engineer applies sound software-engineering QA principles to AIR Lab applications, using Gen AI as a core part of the daily toolkit to design, generate and maintain automated integration, sanity and regression tests. The role is hands-on: turning a deep understanding of each product into executable tests that run in delivery pipelines and give the team fast, trustworthy feedback.

Equally important is judgement. Not every change needs the full suite, and not every proof of concept needs production-grade quality gates. This role decides, together with the Validation Lead and Product Owners, which tests are enforced as blocking gates, which run on a schedule, and where lighter-weight checks are enough, balancing confidence against delivery speed across POCs, MVPs and more mature products.

The role reports to the Head of Engineering and works closely with Product Owners/Functional Leads to understand each product, its users, and how AIR Lab applications talk to one another.

Responsibilities:

• Work closely with Product Owners/Functional Leads to understand product intent, User Journeys, and the ATM operational context behind each application.
• Build and maintain a clear picture of how AIR Lab products interact (interfaces, data flows and dependencies) and use it to pinpoint integration risk.
• Design and implement automated integration tests that verify the contracts and data exchanged between applications and services.
• Build and maintain sanity (smoke) suites that quickly confirm a deployment or environment is healthy before deeper testing begins.
• Build and maintain regression suites that protect agreed User Journeys and previously fixed defects.
• Use AI tools such as Claude to accelerate test work: deriving test cases from User Journeys and specifications, generating test code and test data, and analysing failures and logs.
• Review and verify all AI-generated tests and outputs for correctness, relevance and maintainability before they are committed. AI speeds up the work; the engineer owns the quality.
• Apply a risk-based approach to decide when tests are enforced as blocking gates (merge request, promotion to Demo/Public environments), when they run as non-blocking or scheduled checks, and when lighter verification is sufficient, based on product maturity, change impact and criticality.
• Integrate test suites into GitLab CI/CD pipelines and episodic namespace environments, keeping execution time short and flakiness under control.
• Triage test failures, separate genuine defects from test or environment issues, and raise clear, reproducible defect reports; follow up until resolution.
• Keep test suites lean and maintainable: retire obsolete tests, fix or quarantine flaky ones, and refactor as products evolve.
• Contribute reusable prompts, patterns and guidelines for AI-assisted testing so the wider engineering team can adopt them.
• Report coverage, results and quality risks to the Validation Lead and Product Owners in a clear, actionable format, raising blockers early.
• Own and drive the Software QA lifecycle, raise bugs and ensure quality of the products.

Requirements:

Education

• Bachelor's degree in Computer Science, Information Technology or a related engineering field
• Master's degree in Computer Science or a related field is a plus

Essential Skills/Experience

• 4-6 years of experience in software QA or test automation, with hands-on delivery of integration, sanity and regression suites.
• Solid grasp of software-engineering QA principles: the test pyramid, risk-based testing, test design techniques.
• Hands-on API and service-level test automation (e.g., Karate, Pytest, REST Assured, Postman/Newman) and working proficiency in at least one programming language such as Python or Java.
• Experience integrating automated tests into CI/CD pipelines (GitLab CI preferred) and working with containerised environments (Docker, Kubernetes).
• Ability to understand distributed systems (APIs, messaging, data pipelines) and reason about how components interact and fail.
• Good communication skills i