Software Developer IV (Golang & Kubernetes)

Job description

**About the opportunity** At Rackspace Technology we deliver outcome-based cloud solutions that help customers modernise applications, launch new products, and run complex cloud environments. RackAI is our strategic platform for bringing secure, scalable, cloud-native AI capabilities to customers and internal teams alike. We're looking for an expert **Software Developer IV** to join the PVC Product Software Engineering team and help design and evolve the core of RackAI. You'll pair deep **Go** and **Kubernetes** expertise with genuine architectural leadership — and you'll help set the standard for how a modern engineering team builds software in the age of AI. It's a hands-on senior role: strategic enough to shape direction, close enough to the code to lead by example. Put simply: **we build AI, and we build with AI.** If you want to work at the intersection of cloud-native platform engineering and applied AI — and help define an AI-first engineering culture — this is the seat. **Why RackAI** You'll help shape the future of AI-powered products at Rackspace Technology — building with modern cloud-native technology, solving hard problems at scale, and setting the standard for how engineering teams work in an AI-first world. **What you'll do** **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** - Make AI-assisted engineering the default way of working — champion an **AI-driven, AI-first** approach across the team. - Embed AI development tooling — **Claude Code**, **Kiro**, **Codex**, or **GitHub Copilot** — across the lifecycle, from design and coding through testing, review, and documentation. - Set the standards and guardrails for responsible, effective use of AI coding assistants — code quality, security, licensing, and human-in-the-loop review. - Turn AI augmentation into measurable gains in velocity and quality through prompt engineering and agentic workflows. - Coach engineers to get real leverage from AI tooling, and continually assess what to adopt next. **Engineer the platform and infrastructure** - Design and optimise Kubernetes deployments across multiple environments. - Automate provisioning, deployment, scaling, and lifecycle management. - Partner with Platform Engineering to raise developer productivity and platform reliability. - Ensure solutions meet security, compliance, and operational requirements. **Lead and mentor** - Provide technical leadership across multiple engineering teams. - Lead architectural reviews and design discussions, and set coding standards and patterns. - Mentor Software Developers I–III and grow engineering capability across the group. - Evaluate emerging technologies and recommend strategic investments. **Collaborate across the organisation** - Work closely with Product, Architecture, Data Science, SRE, and Security. - Translate business needs into scalable technical solutions. - Communicate architectural decisions and technical strategy to stakeholders. - Contribute to roadmap planning and long-term platform evolution. **What you'll bring** **Core software engineering** - 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. - Hands-on with container technologies such as Docker and OCI-compliant runtimes. - Solid grounding in software design patterns, CI/CD, DevOps, Git-based workflows, and secure coding practices. **Experience** - Nine or more years in software development, including five or more years writing production Go. - Demonstrable experience building Kubernetes operators or controllers with controller-runtime or kubebuilder. - A track record of delivering production-grade cloud-native applications. - Experience leading complex technical projects end to end, and mentoring engineers to a higher standard. **AI-assisted development** - Hands-on experience with AI coding assistants such as **Claude Code**, **Kiro**, **Codex**, and **GitHub Copilot**, integrated effectively into day-to-day engineering. - A genuine commitment to working within — and helping to shape — an AI-first development culture. **AI and machine learning** - Practical understanding of modern Generative AI and Large Language Models (LLMs). - Familiarity with open-source foundation models such as Llama, Mistral, Gemma, Qwen, or DeepSeek. - 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). - Understanding of Retrieval-Augmented Generation (RAG), vector databases, embeddings, and semantic search. - Experience integrating AI models into production applications using APIs or self-hosted inference platforms. **AI platform and infrastructure** - Experience with containerised AI workloads on Docker and Kubernetes. - Familiarity with AI serving frameworks such as vLLM, Triton Inference Server, TensorRT-LLM, or similar technologies. - Understanding of GPU-accelerated computing and how AI workloads are deployed on modern GPU infrastructure. - Experience deploying scalable AI applications in cloud or private cloud environments. *