{"schemaVersion":"jobsearcher.job.v1","id":"e6b21b8e1ccdd8ec652bb982","url":"https://jobsearcher.com/jobs/e6b21b8e1ccdd8ec652bb982","canonicalUrl":"https://jobsearcher.com/jobs/e6b21b8e1ccdd8ec652bb982","title":"MLops Engineer","description":"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).","company":"Arrayo","rawCompany":"arrayo","city":"Newton Center","state":"MA","isRemote":false,"isActive":false,"createdAt":"2026-05-24T07:27:12.370Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"MLops Engineer","description":"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).","datePosted":"2026-05-24T07:27:12.370Z","dateModified":"2026-05-24T07:27:12.370Z","hiringOrganization":{"@type":"Organization","name":"Arrayo","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Newton Center","addressRegion":"MA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"e6b21b8e1ccdd8ec652bb982"},"url":"https://jobsearcher.com/jobs/e6b21b8e1ccdd8ec652bb982"}}