About this role
Advancing connectivity to secure a brighter world.
Nokia is a global leader in connectivity for the AI era. With expertise across fixed, mobile and transport networks, powered by the innovation of Nokia Bell Labs, we’re advancing connectivity to secure a brighter world.
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As a Senior Software Developer, you will play a key role in driving innovation and delivering impactful solutions. In this position, you will have a significant impact on our organization's success by leading the architecture and development of scalable, cloud-native applications and microservices, while also supporting the integration of AI/ML models into data pipelines.
• Deploy Cloud-Native Apps: containerizing and orchestrating applications via Docker and Kubernetes. • Design Microservices: Develop scalable, event-driven backend microservices using Python, Go, or Java. • Model Network Topologies: Implement robust data models (YANG, OpenConfig) and manage complex network structures using Graph and Time-Series databases. • Data Architecture: Manage highly interconnected network data using Graph databases (Neo4j) and time-series telemetry pipelines. • API & Integration: Create robust gRPC/REST APIs to ingest network state data and expose simulation engine capabilities. • Intelligent Automation: Support the integration of AI/ML models into the data pipeline for predictive analytics.
You have:
• Bachelor’s degree with 8–12 years of experience in cloud-native solution architecture and development. • Strong experience in cloud platforms, cloud-native solution architecture • Hands-on experience containerizing and orchestrating applications via Docker and Kubernetes. • Strong hands-on experience with Docker, Kubernetes, and event streaming (Kafka/RabbitMQ)
It would be nice if you also had:
• Solid understanding of Layer 0/1 optical networking, DWDM, ROADM architectures, and YANG data modeling. • Experience integrating data science/ML frameworks (TensorFlow, PyTorch) into production software pipelines. • Strong knowledge of software engineering best practices, including APIs, automation platforms, version control, testing, CI/CD, deployment, and responsible AI principles related to governance, security, and privacy.