Software Developer IV, Golang, Kubernetes

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Rackspace Technology

Site WebLinkedInTous les Emplois

5001 - 10000 employés

Fondée en 1998

💼 Conseil

📦 Logistique

🤖 Intelligence artificielle

Consulting • Logistics • Artificial Intelligence

Rackspace Technology est un fournisseur mondial de services cloud managés qui conçoit, construit, migre et exploite des environnements de cloud multiple, hybride et privé pour les entreprises. Il offre des services de conseil, professionnels et managés à travers l'infrastructure cloud, la modernisation des applications, les plateformes de données, l'IA/ML (Foundation de l'IA/FAIR), et la cybersécurité, ainsi que l'hébergement managé, la colocation et les intégrations de plateformes avec les principaux hyperscalers. Rackspace dessert des industries réglementées et essentielles en se concentrant sur la sécurité, la conformité, l'optimisation des coûts et la fiabilité opérationnelle.

Description

• Architect and build the RackAI platform • Design and develop highly scalable backend services in Go (Golang) • Build Kubernetes-native applications — operators, controllers, and platform services — using controller-runtime and kubebuilder. • Define and evolve the RackAI platform architecture for growth, reliability, security, and operational excellence. • Develop the APIs, automation frameworks, and platform capabilities that power AI-enabled products. • Champion cloud-native best practices: observability, resiliency, and operational readiness. • Lead an AI-first engineering culture • Coach engineers to get real leverage from AI tooling, and continually assess what to adopt next.

🎯 Exigences

• Nine or more years in software development • Expert-level Go (Golang) development • Deep Kubernetes expertise — architecture, operations, and application development • Proven experience with distributed systems and microservices architectures • Strong command of RESTful APIs, event-driven systems, and service-oriented architecture • Solid grounding in software design patterns, CI/CD, DevOps, Git-based workflows, and secure coding practices. • Hands-on experience with AI coding assistants such as Claude Code , Kiro , Codex , and GitHub Copilot • Practical understanding of modern Generative AI and Large Language Models (LLMs) • Understanding of AI inference concepts, including model serving, latency, throughput, batching, performance improvements, optimizations, benchmarking of model inference. • Knowledge of model fine-tuning techniques, including supervised fine-tuning (SFT), LoRA, and parameter-efficient fine-tuning (PEFT).

🏖️ Avantages

• Health insurance • Flexible work arrangements • Professional development opportunities