MLops Engineer

MLops Engineer (Training Scalability & Workflow Optimization)OverviewWe are seeking an MLops Engineer to lead the scaling of machine learning training pipelines and ensure the robustness and efficiency of our end-to-end ML workflows. This role focuses on leveraging Flyte, Kubernetes (GPU optimization), Docker, and distributed training frameworks such as Ray to optimize and streamline our ML infrastructure.ResponsibilitiesWorkflow Orchestration: Develop and maintain ML workflows using Flyte to manage complex ML pipelines for training, testing, and deployment.Training Scalability: Architect and scale large-scale ML training systems on GPU-backed Kubernetes clusters, including auto-scaling and performance tuning for multi-node/multi-GPU workloads.Distributed Computing: Implement distributed model training pipelines using frameworks like Ray for parallelization and resource efficiency.Containerization: Design, build, and optimize Docker images for ML workloads with a focus on reproducibility and security.Resource Optimization: Debug and optimize GPU utilization, memory, and compute bottlenecks during training and inference phases.Monitoring & Maintenance: Integrate monitoring for ML jobs, track resource consumption, and enforce cost-efficient resource utilization.Collaboration: Work closely with data scientists and ML engineers to productize and scale ML experiments.QualificationsStrong proficiency with Kubernetes (GPU scheduling, Helm, cluster autoscaling).Hands-on experience with Flyte or similar workflow orchestration tools (Airflow, Prefect).Deep knowledge of distributed ML training (e.g., PyTorch DDP, Ray, Horovod).Expertise in Docker and container lifecycle management.Solid understanding of GPU hardware/software stack (CUDA, NCCL).Familiarity with CI/CD for ML (MLops pipelines using tools like GitHub Actions, ArgoCD).Bonus: Familiarity with observability tools for ML systems (Prometheus, Grafana).