Company:
Qualcomm Korea YH
Job Area:
Engineering Group, Engineering Group > Machine Learning Engineering
General Summary:
We are seeking a highly skilled and experienced Senior MLOps Engineer to join our team and contribute to the development and maintenance of our ML platform. As a Senior MLOps Engineer, you will be responsible for architecting, deploying, and optimizing the ML platform that supports training of Machine Learning Models using NVIDIA DGX clusters and the Kubernetes platform, including technologies like Helm, ArgoCD, Argo Workflow, Prometheus, and Grafana. You will work closely with cross-functional teams, including data scientists, software engineers, and infrastructure specialists, to ensure the smooth operation and scalability of our ML infrastructure. Your expertise in MLOps and knowledge of GPU clusters will be vital in enabling efficient training and deployment of ML models.
Responsibilities:
Architect, develop, and maintain the ML platform to support training and inference of ML models.
Design and implement scalable and reliable infrastructure solutions for NVIDIA DGX clusters.
Collaborate with data scientists and software engineers to define requirements and ensure seamless integration of ML workflows into the platform.
Optimize the platform’s performance and scalability, considering factors such as GPU resource utilization, data ingestion, model training, and deployment.
Monitor and troubleshoot system performance, identifying and resolving issues to ensure the availability and reliability of the ML platform.
Implement and maintain CI/CD pipelines for automated model training, evaluation, and deployment using technologies like ArgoCD and Argo Workflow.
Implement and maintain monitoring stack using Prometheus and Grafana to ensure the health and performance of the ML platform.
Stay updated with the latest advancements in MLOps, distributed computing, and GPU acceleration technologies, and proactively propose improvements to enhance the ML platform.
Provide technical guidance and mentorship to junior team members.
Qualifications:
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Proven experience as an MLOps Engineer or similar role, with a focus on large-scale ML infrastructure and GPU clusters.
Strong expertise in configuring and optimizing NVIDIA DGX clusters for deep learning workloads.
Proficient in using the Kubernetes platform, including technologies like Helm, ArgoCD, Argo Workflow, Prometheus, and Grafana.
Solid programming skills in languages like Python, and experience with relevant ML frameworks (e.g., TensorFlow, PyTorch).
In-depth understanding of distributed computing, parallel computing, and GPU acceleration techniques.
Familiarity with containerization technologies such as Docker and orchestration tools.
Experience with CI/CD pipelines and automation tools for ML workflows (e.g., Jenkins, GitHub, ArgoCD).
Strong problem-solving skills and the ability to troubleshoot complex technical issues.
Excellent communication and collaboration skills to work effectively within a cross-functional team.
Preferred Skills:
Experience with training and deploying models for Automated Driving.
Knowledge of ML model optimization techniques and memory management on GPUs.
Familiarity with ML-specific data storage and retrieval systems .
Understanding of security and compliance requirements in ML infrastructure.
Minimum Qualifications:
• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Science, Engineering, Information Systems, or related field.
Applicants: If you need an accommodation, during the application/hiring process, you may request an accommodation by sending email toaccommodationsupport
Although this role has some expected minor physical activity, this should not deter otherwise qualified applicants from applying. If you are an individual with a physical or mental disability and need an accommodation during the application/hiring process, please call Qualcomm’s toll-free number found herefor assistance. Qualcomm will provide reasonable accommodations, upon request, to support individuals with disabilities as part of our ongoing efforts to create an accessible workplace.
Qualcomm is an equal opportunity employer and supports workforce diversity.
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
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