Senior MLOps Engineer

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

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.