4 hours ago
Remote, IndiaStaff+
Responsibilities
- Architect and build the RackAI platform and scalable backend services in Go.
- Develop Kubernetes-native applications, including operators, controllers, and platform services using controller-runtime and kubebuilder.
- Define platform architecture and develop APIs, automation frameworks, and capabilities for AI-enabled products.
- Design and optimize Kubernetes deployments, automate provisioning and lifecycle management, and improve platform reliability.
- Establish standards and guardrails for AI-assisted engineering using tools such as Claude Code, Kiro, Codex, and GitHub Copilot.
- Lead architectural reviews, design discussions, coding standards, and technical strategy across multiple engineering teams.
- Mentor Software Developers I–III and evaluate emerging technologies and strategic investments.
- Collaborate with Product, Architecture, Data Science, SRE, and Security on scalable technical solutions and roadmap planning.
Requirements
- Expert-level Go development and deep Kubernetes expertise covering architecture, operations, and application development.
- At least nine years of software development experience, including at least five years writing production Go.
- Experience building Kubernetes operators or controllers with controller-runtime or kubebuilder.
- Proven experience delivering production-grade cloud-native applications and leading complex technical projects end to end.
- Experience mentoring engineers and providing technical leadership across teams.
- Experience with distributed systems, microservices architectures, RESTful APIs, event-driven systems, service-oriented architecture, Docker, and OCI-compliant runtimes.
- Strong grounding in software design patterns, CI/CD, DevOps, Git-based workflows, and secure coding practices.
- Hands-on experience using AI coding assistants in day-to-day engineering and commitment to an AI-first development culture.
- Practical understanding of generative AI, LLMs, model serving, inference performance, fine-tuning, RAG, vector databases, embeddings, semantic search, and production AI integrations.
- Experience with containerized AI workloads, scalable AI deployments, GPU-accelerated computing, and AI serving frameworks such as vLLM, Triton Inference Server, or TensorRT-LLM.
- Preferred qualifications include experience building AI/ML platforms or AI-enabled enterprise products, advanced CRD and operator work, AWS, Azure or Google Cloud, service mesh technologies, platform engineering, observability stacks, and open-source contributions.
Benefits
- The role is part of Rackspace Technology’s RackAI platform and offers the opportunity to shape AI-powered products and an AI-first engineering culture.
- The position provides technical leadership, architectural ownership, and opportunities to mentor engineers across multiple teams.
- Rackspace Technology states that it offers equal employment opportunity and accommodations for applicants with disabilities or special needs.
About Rackspace
Rackspace Technology® is the operator of the full enterprise AI stack from governed private cloud to AI inference and agents in production. With an Outcomes-as-a-Service model built on secure infrastructure, data foundations and forward-deployed engineering, we deliver business results for regulated and mission-critical industries where governance, sovereignty and uptime are non-negotiable.