DevOps & Cloud Infrastructure Engineer

WorkSprout is a deep-tech engineering and design partner across twelve practice areas — TinyML & Edge AI, Custom AI Development, AI Automation & Agents, Data Engineering & Collection, IoT Solutions, Hardware Prototyping, Robotics & Automation, 3D Design & Modeling, Software Development, Growth & DevOps, Branding & Creative Design, and Startup & Product Launch. We deliver systems that work in production, not just in demos.

You will own the infrastructure, CI/CD pipelines, and deployment automation that keeps WorkSprout client systems running reliably at production scale. From containerised microservices on Kubernetes to ML model serving infrastructure and IoT backend platforms, you will build the operational foundation that engineering teams rely on to ship fast without breaking things.

• Design and maintain Kubernetes clusters and Docker-based deployment environments
• Build and own CI/CD pipelines using GitHub Actions, GitLab CI, or Jenkins
• Manage cloud infrastructure on AWS or GCP using Terraform or Pulumi (IaC)
• Implement monitoring, alerting, and observability stacks with Prometheus, Grafana, and ELK
• Configure secure networking, VPCs, load balancers, and TLS certificate management
• Establish and enforce security hardening, secret management, and access control policies
• Support ML platform infrastructure — GPU node pools, model serving, and training job scheduling

• 3+ years of DevOps or platform engineering experience in production environments
• Strong Docker and Kubernetes administration experience (EKS, GKE, or self-managed)
• Infrastructure-as-Code proficiency with Terraform or Pulumi
• Experience with CI/CD pipeline design and GitOps workflows
• Knowledge of cloud networking, IAM, secrets management, and security best practices

• Experience with NVIDIA GPU Operator and Kubernetes-based ML workload scheduling
• Familiarity with service mesh tooling (Istio, Linkerd) for microservice security
• Knowledge of cost optimisation strategies for cloud-native and spot-instance workloads
• Background in SRE practices — SLOs, error budgets, and incident management

• Own the infrastructure layer for diverse production AI, IoT, and software systems
• Competitive salary benchmarked to international market rates
• Flexible hybrid work arrangement with modern tooling budget
• Work on infrastructure serving clients across AI, robotics, IoT, and software products
• Direct access to cloud compute budget for experimentation and optimisation

* Benefits marked with an asterisk apply to permanent employees only.

If your experience matches the requirements and you are ready to work on production engineering and design challenges, submit your application using the button below. We review every submission personally — no automated screening, no ghosting.