Engineer- Machine Learning

Job Description

Job Area: Engineering Group, Engineering Group > Software Engineering General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs. Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces. Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or related field. Job Title: MLOps Engineer - ML Platform Hiring Title: Flexible based on candidate experience – about Staff Engineer preferred : We are seeking a highly skilled and experienced MLOps Engineer to join our team and contribute to the development and maintenance of our ML platform both on premises and AWS Cloud. As a MLOps Engineer, you will be responsible for architecting, deploying, and optimizing the ML & Data 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. Your expertise in AWS services such as EKS, EC2, VPC, IAM, S3, and EFS will be crucial in ensuring the smooth operation and scalability of our ML infrastructure. 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, DevOps, and knowledge of GPU clusters will be vital in enabling efficient training and deployment of ML models. Responsibilities will include: 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 clusters both on premises and AWS Cloud. Collaborate with data scientists and software engineers to define requirements and ensure seamless integration of ML and Data 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 platform. Manage AWS services including EKS, EC2, VPC, IAM, S3, and EFS to support the platform. Implement logging and monitoring solutions using AWS CloudWatch and other relevant tools. Stay updated with the latest advancements in MLOps, distributed computing, and GPU acceleration technologies, and proactively propose improvements to enhance the ML platform. What are we looking for: 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 and/or Data 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, Go 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). Experience with AWS services such as EKS , EC2, VPC, IAM, S3, and EFS. Experience with AWS logging and monitoring tools. 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. We would love to see: Experience with training and deploying models. 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.