MLOps Engineer (PyTorch)

Job Title: MLOps Engineer (PyTorch) Location: Singapore Job Type: Full-time About the Opportunity Our client is seeking an MLOps Engineer with a strong background in systems programming and infrastructure engineering. This role is focused on owning and evolving the on-premise infrastructure that powers their advanced PyTorch -based training workloads. This position is a perfect fit for an engineer who is not just focused on model outcomes, but on the quality and robustness of the underlying systems. You will be responsible for building high-quality, maintainable training pipelines, solving low-level systems and networking challenges, and ensuring the training codebase is clean, scalable, and built to last. Key Responsibilities Architect, build, and maintain end-to-end training and inference pipelines using PyTorch . Develop and maintain high-quality, robust tooling in both Python and C++ to support the entire model training lifecycle. Take full ownership of the core training codebase , enforcing best practices for clarity, modularity, reproducibility , and performance. Design and implement workflows for checkpointing , resuming jobs, model versioning, and experiment tracking. Proactively optimize compute workloads for bare-metal environments, focusing on I/O bottlenecks, CPU/GPU utilization , and memory efficiency. Troubleshoot and debug complex, low-level issues , including networking bottlenecks , distributed training errors (e.g., NCCL ), and hardware faults. Configure and manage all ML environments , including containers , package management, GPU drivers , and runtime configurations. Monitor and debug large-scale training jobs running across multiple nodes and GPUs. Required Qualifications (You Should Have) Deep, expert-level knowledge of PyTorch , including DDP (DistributedDataParallel), mixed precision training, and TorchScript . Advanced programming skills in both C++ and Python . A solid background in computer science fundamentals (data structures, algorithms, concurrency , operating systems). Hands-on experience debugging and tuning bare-metal servers , including Linux administration, kernel parameter tuning , and BIOS tuning . A strong understanding of low-level networking (e.g., RoCE, InfiniBand), interconnects, and distributed training protocols like NCCL and MPI . A proven track record of building reliable, reproducible pipelines for both model training and evaluation. Experience with job schedulers (e.g., SLURM , or custom runners) and cluster monitoring tools. Preferred Qualifications (Nice-to-Have) Experience with non-standard deployments, such as on-premise local clusters or edge devices (i.e., not public cloud). Active contributions to PyTorch or other open-source ML/HPC tools. Familiarity with Infrastructure-as-Code (IaC) tools like Ansible , Terraform , or Nix . Experience building out a full logging, observability, and alerting stack for training workloads. How to Apply …