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MLops Engineer

Arrayo
Boston
4 weeks ago
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MLOps Engineer

MLOps Engineer (Kubernetes, Cloud, ML Workflows)

Senior MLOps Engineer

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Senior/ Principal Data Engineering Consultant- London

MLops Engineer (Training Scalability & Workflow Optimization)

Overview

We are seeking anMLops Engineerto 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 leveragingFlyte,Kubernetes (GPU optimization),Docker, and distributed training frameworks such asRayto optimize and streamline our ML infrastructure.

Responsibilities

  • Workflow Orchestration:Develop and maintain ML workflows usingFlyteto manage complex ML pipelines for training, testing, and deployment.
  • Training Scalability:Architect and scale large-scale ML training systems onGPU-backed Kubernetes clusters, including auto-scaling and performance tuning for multi-node/multi-GPU workloads.
  • Distributed Computing:Implement distributed model training pipelines using frameworks likeRayfor 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 withKubernetes(GPU scheduling, Helm, cluster autoscaling).
  • Hands-on experience withFlyteor similar workflow orchestration tools (Airflow, Prefect).
  • Deep knowledge of distributed ML training (e.g., PyTorch DDP, Ray, Horovod).
  • Expertise inDockerand 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).

Seniority level

  • Seniority levelMid-Senior level

Employment type

  • Employment typeFull-time

Job function

  • Job functionEngineering and Information Technology
  • IndustriesBusiness Consulting and Services, Biotechnology Research, and Engineering Services

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