MLops Engineer

MLops Engineer (Training Scalability & Workflow Optimization) Overview We 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. Responsibilities Workflow 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. Qualifications Strong 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). #J-18808-Ljbffr