Come work at a place where innovation and teamwork come together to support the most exciting missions in the world!
We are seeking a highly skilled and passionate Senior Software Engineer to join Qualys' next-generation Platform & Middleware team. This role is primarily focused on designing and developing scalable Java-based backend services and platform components while taking end-to-end ownership of software delivery—from design and development to testing, automation, deployment, and production support.
We believe software engineers own the quality of the features they build. As part of feature ownership, engineers are expected to validate their work through automated testing, release validation, and continuous improvement of engineering practices. While backend software development remains the primary responsibility, the engineer should be comfortable contributing to test automation, release readiness, and production reliability as required by the team.
Responsibilities
- Design and develop scalable backend services, platform components, and middleware systems using Java and related technologies.
- Build reliable REST APIs, distributed services, and cloud-native applications for large-scale SaaS environments.
- Own features throughout the entire software development lifecycle, including design, implementation, testing, deployment, monitoring, and production support.
- Write high-quality, maintainable, and well-tested code with a strong focus on performance, scalability, and reliability.
- Develop and maintain unit, integration, API, and end-to-end automated tests as part of feature development.
- Build and enhance API and UI automation using PyTest, Playwright, and other appropriate automation frameworks to ensure product quality.
- Perform functional, integration, regression, and release validation for developed features.
- Contribute to CI/CD pipelines, deployment automation, and release engineering activities.
- Participate in architecture discussions, design reviews, and technical decision-making for platform services.
- Troubleshoot complex issues across distributed services, databases, messaging systems, and cloud infrastructure.
- Improve system scalability, resiliency, observability, and operational excellence.
- Participate in code reviews, root cause analysis, and production support.
- Contribute to engineering best practices, coding standards, and mentor junior engineers where appropriate.
- Explore and adopt modern engineering productivity practices and AI-assisted development tools where applicable.
Required Skills
- 3–7 years of software development experience.
- Strong Java programming skills with a solid understanding of object-oriented design, data structures, algorithms, concurrency, and system design.
- Experience building REST APIs, microservices, and distributed systems.
- Strong commitment to software quality through unit, integration, API, and end-to-end automated testing.
- Experience with test automation frameworks such as PyTest, JUnit, RestAssured, Playwright, Selenium, or similar technologies.
- Experience with messaging technologies such as Kafka, RabbitMQ, or similar platforms.
- Experience with CI/CD tools and modern deployment practices using Jenkins, GitLab CI, Docker, Kubernetes, and Helm.
- Strong understanding of relational databases, SQL optimization, and data modeling.
- Exposure to NoSQL technologies such as Cassandra, MongoDB, or Redis.
- Strong debugging, troubleshooting, and analytical problem-solving skills.
- Good understanding of scalable, resilient, observable, and production-grade distributed systems.
- Excellent communication and collaboration skills with the ability to work across development, testing, and operations teams.
Preferred Skills
- Experience working on SaaS, cloud security, or enterprise platform products.
- Familiarity with AI-assisted software development tools such as GitHub Copilot, Cursor, Windsurf, Claude Code, or similar tools.
- Experience with performance engineering, observability, and monitoring platforms.
- Nice to have: familiarity with emerging AI engineering concepts such as MCP integrations, agentic workflows, RAG pipelines, AI-powered developer tooling, or LLM-based automation.
- Understanding of Site Reliability Engineering (SRE) principles and production reliability engineering.
Education
- Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.