Software Developer IV, Golang, Kubernetes

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Rackspace Technology

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 1998

💼 Consulting

📦 Logistics

🤖 Artificial Intelligence

Consulting • Logistics • Artificial Intelligence

Rackspace Technology is a global managed cloud services provider that designs, builds, migrates and operates multicloud, hybrid and private cloud environments for enterprises. It offers consulting, professional and managed services across cloud infrastructure, application modernization, data platforms, AI/ML (Foundry of AI/FAIR), and cybersecurity, along with managed hosting, colocation and platform integrations with major hyperscalers. Rackspace serves regulated and mission-critical industries with a focus on security, compliance, cost optimization and operational reliability.

📋 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.

🎯 Requirements

• 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).

🏖️ Benefits

• Health insurance • Flexible work arrangements • Professional development opportunities