Design and deploy edge computing solutions that extend cloud capabilities to distributed locations, enabling low-latency processing for IoT, AI inference, and real-time analytics workloads.
Amsterdam, Netherlands
Full-time
Cloud & Infrastructure
Responsibilities
Architect edge computing solutions using AWS Outposts, Azure Stack Edge, or Google Distributed Cloud for latency-sensitive workloads
Design and implement lightweight Kubernetes distributions (K3s, MicroK8s) for edge site management at scale
Build edge-to-cloud data pipelines for IoT telemetry aggregation, edge AI inference, and real-time analytics
Implement edge device fleet management including automated provisioning, updates, and security patching
Develop edge application deployment strategies with offline resilience and eventual consistency patterns
Optimize edge infrastructure for power, bandwidth, and compute constraints in distributed environments
Requirements
5-8 years of infrastructure engineering experience with at least 2 years focused on edge or IoT platforms
Experience with edge computing platforms (AWS Greengrass, Azure IoT Edge, Google Distributed Cloud Edge)
Strong Kubernetes knowledge with experience deploying lightweight distributions to resource-constrained environments
Understanding of edge networking including SD-WAN, 5G integration, and content delivery architectures
Proficiency in Go, Rust, or C++ for building resource-efficient edge applications and agents
Experience with real-time data processing frameworks and edge AI inference optimization
Nice to Have
Experience with WebAssembly (Wasm) for portable edge computing workloads
Knowledge of MQTT, AMQP, or other IoT messaging protocols
Skills
Edge ComputingKubernetesIoTGoAWSAzureReal-time ProcessingDistributed Systems
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