Senior MLOps Engineer

Required Experience: 3 to 6 Years

Roles And Responsibilities

Design, deploy, and maintain distributed systems using Kubernetes and Slurm for optimal resource utilization and workload management.
Lead the configuration and optimization of Multi-GPU, Multi-Node Deep Learning job scheduling, ensuring efficient computation and data processing.
Collaborate with cross-functional teams to understand project requirements and translate them into technical solutions.
Experience in working with On-prem NVIDIA GPU servers.
Develop and maintain complex shell scripts for various system automation tasks, enhancing efficiency and reducing manual intervention.
Monitor system performance, identify bottlenecks, and implement necessary adjustments to ensure high availability and reliability.
Troubleshoot and resolve technical issues related to the distributed system, job scheduling, and deep learning processes.
Stay updated with industry trends and emerging technologies in distributed systems, deep learning, and automation.

Skill Set Needed

Strong communication and collaboration skills to work effectively within a cross-functional team.
Good with Python. 
Hands-on experience in MLOps - MLFlow, Kubeflow, AutoML etc.
Good to have at least one ML framework understanding - PyTorch / TensorFlow.
Experience in shell scripting./linux
Good understanding of logical networks. 
Understanding of NLP (preferred) / Computer Vision
Cloud native stack.
Proven experience in designing, deploying, and managing distributed systems, with a focus on Kubernetes and Slurm.
Sufficient understanding of AI Model Training and Deployment and Strong background in Multi-GPU, Multi-Node Deep Learning job scheduling and resource management.
Proficiency in Linux systems, particularly Ubuntu, and the ability to navigate and troubleshoot related issues.
Extensive experience creating complex shell scripts for automation and system orchestration.
Familiarity with continuous integration and deployment (CI/CD) processes.
Excellent problem-solving skills and the ability to diagnose and resolve technical issues promptly.

Good To Have

Previously working on NVIDIA Ecosystem or well aware of NVIDIA Ecosystem - Triton Inference Server, CUDA, 
Good at Slurm, Kubernetes, Linux, and AI Deployment tools.