Sobre este puesto de Senior AI-Driven Integration & Validation Engineer en Parallelwireless
Our Integration & Validation team plays a critical role in ensuring that complex distributed systems work seamlessly together before reaching our customers. We don't just verify functionality—we develop a deep understanding of how the entire system behaves, investigate complex technical challenges, and continuously improve the quality and reliability of our products.
We're looking for engineers who are curious, innovative, and passionate about solving difficult system-level problems while embracing AI as part of modern engineering.
What you'll do:
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Design and execute end-to-end integration and validation scenarios across multiple product domains.
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Validate new features and ensure seamless interoperability between distributed system components.
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Investigate complex cross-component issues through deep technical analysis and root-cause investigation.
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Analyze application and system logs to identify issues and drive resolution.
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Develop a strong understanding of system architecture, data flows, and interactions between services.
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Collaborate closely with Software Engineers, Architects, Product Managers, and cross-functional engineering teams.
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Participate in feature planning by providing an integration and validation perspective early in the development lifecycle.
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Continuously improve validation methodologies, engineering processes, and overall product quality.
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Leverage AI tools to accelerate troubleshooting, technical research, documentation, and engineering productivity.
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Challenge existing approaches and introduce innovative ideas that improve engineering efficiency and product quality
What you should have:
Nice to have:
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Experience with Python or another scripting language.
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Experience with test automation frameworks such as Robot Framework or PyTest.
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Docker and Kubernetes experience.
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Experience with telecom technologies (4G, 5G, Open RAN).
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Familiarity with cloud-native architectures and microservices.
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Experience using observability platforms such as Grafana, Prometheus, Kibana, or Elasticsearch.
Education:
- B.Sc. in Computer Science, Software Engineering, Electrical Engineering, Computer Engineering, or a related technical discipline.
