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.

• 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.

• 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 Git Hub Actions, ArgoCD).
• Bonus:
  Familiarity with observability tools for ML systems (Prometheus, Grafana).