Senior Software Engineer – AI & Temporal
New
R
Ready4STelecommunications
100% remote workFull-TimeSenior
Salary30,000 - 40,000 PLN per month
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Job Details
- Languages
- English (B2)
- Experience
- 6+ years
- Required Skills
- DockerGoRESTful APIsMicroservicesDistributed Systems
Requirements
- 6+ years of commercial experience in Software Engineering / Backend Development.
- Hands-on experience with Temporal or similar workflow orchestration technologies such as Cadence.
- Strong understanding of workflow orchestration concepts, asynchronous processing and long-running processes.
- Strong experience with AI-assisted software development and tools such as Claude Code, GitHub Copilot, Cursor or similar.
- Ability to demonstrate concrete examples of how AI has improved your software development workflow and productivity.
- Understanding of different AI models, their capabilities, limitations and appropriate use cases.
- Strong backend software engineering fundamentals.
- Experience designing and implementing REST APIs.
- Experience with distributed systems, microservices and asynchronous architectures.
- Strong debugging and problem-solving skills across distributed environments.
- Experience with Docker and container-based development workflows.
- Ability to work independently and collaborate effectively within a cross-functional engineering team.
Responsibilities
- Design and develop scalable backend systems and services.
- Build and maintain Temporal workflows and activities supporting complex business processes.
- Design resilient solutions for long-running and asynchronous processes.
- Work with distributed systems, including retries, failures, timeouts and communication between services.
- Design and optimize REST APIs and system integrations.
- Use AI coding tools extensively throughout the software development lifecycle.
- Build codebases and documentation that are easy for both engineers and AI tools to understand and modify.
- Create detailed specifications, prompts, reusable AI workflows, skills and development practices.
- Experiment with different AI models and tools to identify the best approach for specific engineering tasks.
- Share AI development practices and reusable solutions across the engineering team.
- Troubleshoot and debug complex issues across service boundaries.
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