Back to SearchLead DevOps/MLOps Engineer
Remote in Belarus, & 4 others
Data DevOps& 10 others
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We are looking for a Lead DevOps/MLOps Engineer to join our Data Science Department and work with the ML Engineering team to build out and harden our on-premises, GPU-backed ML platform. In this role, you will help enable multiple ML and LLM use cases to run simultaneously, reliably, and with low latency, while scaling the platform to support a wider user base across the organization.
Responsibilities
- Build and harden the on-premises GPU-backed ML platform to support concurrent ML and LLM workloads
- Troubleshoot and resolve daily resource bottlenecks and latency issues across competing production and development use cases
- Deploy and maintain the NVIDIA GPU Operator and manage GPU resources across Kubernetes and OpenShift clusters
- Implement GPU sharing and isolation strategies to optimize memory and fault isolation across workloads
- Scale the GPU environment to accommodate growing compute demands as new H200 servers are added
- Maintain and improve platform services such as MLflow and the Agent Gateway
- Manage monitoring pipelines using NVIDIA DCGM exporter, Prometheus and Grafana to track GPU utilization and performance
- Collaborate with the Deep Learning and Traditional ML teams to support shared infrastructure, including Ray-based training workloads
- Ensure platform reliability and security given the confidential and sensitive nature of data handled on-premises
- Partner with the Data Analytics team to support the in-house agentic platform as it scales to a broader employee base
Requirements
- 5+ years of experience running GPU workloads on Kubernetes or OpenShift in production, ideally on-premises
- Practical experience deploying and troubleshooting the NVIDIA GPU Operator, with clear understanding of each component's function
- Understanding of how GPUs differ from CPUs as a scheduled resource, including memory limits, utilization semantics and scheduling limitations
- Knowledge of GPU sharing and isolation methods such as MIG, time-slicing and MPS, along with their trade-offs
- Solid Linux fundamentals, including cgroups, namespaces, systemd resource controls and process-level resource management
- Experience with Prometheus, Grafana and NVIDIA DCGM, including GPU metrics at process or pod level
- Working knowledge of the CUDA stack, including drivers, CUDA toolkit and container runtime version compatibility
- Experience deploying with Helm and operating MLOps tools such as MLflow and Airflow on Kubernetes
Nice to have
- Familiarity with LLM serving engines such as vLLM, SGLang or NVIDIA Triton
- Experience with Ray, KubeRay and GPU-aware schedulers such as Kueue or Volcano
- Hands-on work with H100/H200-class hardware and GitLab CI