DevOps Engineer
Company not named
Hiring a DevOps Engineer to own, scale, and secure cloud and on-premise computing environments, with a focus on GPU infrastructure and MLOps workflows. The role works closely with ML researchers, data scientists, and software engineers to optimize model training, GPU cluster utilization, and CI/CD automation. Position is full-time, hybrid (3 days a week in the office) in Hong Kong.
What you’ll do
- Provision, monitor, and scale high-performance GPU clusters for training and inference
- Support model deployment, fine-tuning, and low-latency inference optimization using open LLM ecosystems
- Design, build, and maintain CI/CD pipelines for software services and ML model artifacts
- Manage cloud resources repeatably using IaC tools
- Deploy, scale, and manage distributed containerized workloads using Kubernetes and Docker
- Implement logging, tracing, and metric dashboards to track cluster health and inference latency
- Enforce DevSecOps practices, secret management, and container image scanning
What they’re looking for
- 3+ years experience in DevOps, SRE, or Cloud/Platform Engineering with exposure to MLOps or AI/ML workloads
- Hands-on experience with AWS, GCP, or Azure and production Kubernetes and Docker management
- Proficiency in Python, Bash/Shell, or Go for automation and tooling
- Demonstrated mastery of Terraform, GitOps workflows, and automated testing/deployment pipelines
- Familiarity with GPU node provisioning, distributed storage for massive datasets, and model serving runtimes
- Preferred: experience with LangChain, Ray Serve, vLLM
- Preferred: Bachelor's or Master's degree in a related technical field
- Preferred: active cloud or Kubernetes certifications (e.g., CKA, AWS/GCP DevOps Engineer)
Skills
- NVIDIA GPUs / CUDA / ROCm: NVIDIA H100, A100, CUDA, ROCm, GPU cluster provisioning, GPU memory/utilization
- Model serving & LLM runtimes: vLLM, Triton Inference Server, Llama, Mistral, low-latency inference
- CI/CD: GitHub Actions, GitLab CI, ArgoCD, CI/CD pipelines, GitOps
- Infrastructure as Code: Terraform, Pulumi, IaC, cloud resource management
- Cloud providers: AWS, GCP, Azure
- Containerization & orchestration: Kubernetes, K8s, Docker, KubeFlow, Ray
- Observability: Prometheus, Grafana, OpenTelemetry, logging, tracing, metrics
- Security & secrets: DevSecOps, HashiCorp Vault, container image scanning, access controls
- Scripting & programming: Python, Bash, Shell, Go
- LLM orchestration frameworks (preferred): LangChain, Ray Serve, vLLM
The post
DevOps Engineer Hybrid, 3 days a week in the office Hong Kong Full-time
We are looking for a DevOps Engineer to own, scale, and secure our cloud and on-premise computing environments. You will work closely with ML researchers, data scientists, and software engineers to streamline model training, optimize GPU cluster utilization, and automate our continuous integration and deployment (CI/CD) pipelines.
Core Responsibilities AI & GPU Infrastructure Management: Provision, monitor, and scale high-performance GPU clusters (NVIDIA H100/A100, CUDA/ROCm setups) for heavy model training and inference workloads. MLOps & Model Serving Pipelines: Support model deployment, fine-tuning, and low-latency inference optimization using open LLM ecosystems (e.g., vLLM, Triton Inference Server, Llama, Mistral). CI/CD & Automation: Design, build, and maintain robust CI/CD pipelines for both software services and ML model artifacts using GitHub Actions, GitLab CI, or ArgoCD. Infrastructure as Code (IaC): Manage cloud resources (AWS, GCP, or Azure) repeatably using tools like Terraform or Pulumi. Containerization & Orchestration: Deploy, scale, and manage distributed containerized workloads using Kubernetes (K8s), Docker, and KubeFlow or Ray. Observability & Reliability: Implement logging, tracing, and metric dashboards (Prometheus, Grafana, OpenTelemetry) to track cluster health, GPU memory/utilization, and inference latency. Security & Governance: Enforce DevSecOps practices, secret management (HashiCorp Vault), container image scanning, and access controls tailored for sensitive AI datasets and models. Required Qualifications Experience: 3+ years of experience in DevOps, Site Reliability Engineering (SRE), or Cloud/Platform Engineering, with exposure to MLOps or AI/ML workloads. Cloud & Containers: Strong hands-on experience with AWS, GCP, or Azure, alongside production Kubernetes and Docker management. Scripting & Programming: Proficiency in Python, Bash/Shell, or Go for automation and tooling. IaC & CI/CD: Demonstrated mastery of Terraform, GitOps workflows, and automated testing/deployment pipelines. AI Ecosystem Knowledge: Familiarity with GPU node provisioning, distributed storage for massive datasets, and model serving runtimes. Preferred Qualifications Experience with LLM orchestration frameworks (LangChain, Ray Serve, vLLM). Bachelor's or Master’s degree in Computer Science, Computer Engineering, or a related technical field. Active cloud or Kubernetes certifications (e.g., CKA, AWS/GCP DevOps Engineer).
Apply: https://round1.work/listings/listing_tenant_a741427d-313d-4108-9e6e-ddd133d4f386