2 months ago
Java/Kotlin Software Engineer (Quantitative Trading)
Vacancy from Hirify Global, list of international tech companiesPlus is required to make matches and apply
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Job description
Text:
TL;DR
Java/Kotlin Software Engineer (Quantitative Trading): Building and maintaining distributed services for ingesting, processing, and distributing financial market reference data with an accent on Java modernization, platform reliability, and cloud-native infrastructure. Focus on upgrading legacy JDK applications, improving observability and performance, and operating Kubernetes-based systems that support quantitative research and trading platforms.
Location: London, UK; hybrid work with office attendance required 4 days per week for 3 weeks of the month and 5 days during the final week.
Company
Quantitative trading and technology firm developing research and trading infrastructure.
What you will do
- Develop and maintain distributed services for ingesting and processing market reference data.
- Deliver Java and JDK upgrade projects across production applications.
- Improve the reliability, scalability, performance, and security of distributed systems.
- Enhance platform observability with telemetry and monitoring tools.
- Work with Kubernetes services, deployment pipelines, CI/CD, and cloud-native infrastructure.
- Collaborate with software engineers, platform teams, and quantitative researchers while using AI-assisted development tools.
Requirements
- Commercial software engineering experience with Java.
- Experience upgrading or modernizing Java applications, preferably through JDK migrations.
- Experience building distributed systems or backend services.
- Familiarity with Kubernetes and containerized applications.
- Exposure to observability tools such as OpenTelemetry or similar technologies.
- Interest in building reliable and scalable infrastructure for high-performance systems.
Nice to have
- Experience with Kotlin and/or Python.
Culture & Benefits
- Permanent employment.
- Modern software development practices and cloud-native engineering.
- AI-assisted development tools are used to improve productivity and delivery speed.
- Close collaboration with platform engineering and quantitative research teams.
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