MLops Engineer
Arrayo
Job description
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).