Senior MLOps Engineer - Security and Networking Research

You will join a group that specializes in Security and Networking areas in relation to ML/AI development. Building and maintaining the infrastructure, tools, and processes necessary to support the machine learning (ML) lifecycle in a production environment. Collaborate closely with data scientists, software engineers, and DevOps teams to ensure the smooth deployment, monitoring, and optimization of ML models. This role involves problem-solving alongside engineering teams and contributing to the development of a successful NVIDIA practice!

What You'll Be Doing

• Develop and maintain scalable infrastructure for handling and deploying security and networking ML models in production, ensuring high availability, scalability, performance.
• Design and implement data pipelines to efficiently process and transform large volumes of data for training and inference purposes.
• Optimize and fine-tune ML models for performance, scalability, and resource utilization, considering factors such as latency, efficiency, and cost.
• Collaborate with data scientists and software engineers to operationalize and deploy ML models, including model versioning, packaging, and integration with existing systems.
• Collaborate with DevOps teams to integrate ML pipelines and workflows into the overall CI/CD process, ensuring flawless deployments and rollbacks.
• Automate the training and retraining processes, ensuring regular model updates and improvements based on new data and performance feedback.
• Implement and manage A/B testing frameworks to evaluate and compare the efficiency of different ML models or algorithmic approaches.
• Build and maintain monitoring and alerting systems to proactively identify and resolve issues related to model performance, data quality, and infrastructure.
• Implementing access controls, authentication mechanisms, and encryption standards for ML models and data.
• Document, guidelines, and standard operating procedures for ML Ops processes and share knowledge with the wider team.
  
  

• Bachelor’s or master’s degree in computer science, data science, or a related field.
• Strong background in machine learning, with experience in deploying and maintaining ML models in a production environment - at least 6 years of experience
• Proficiency in programming languages such as Python, Java, or Scala, along with experience in using ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience with containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, Azure, GCP) for deploying and scaling ML applications.
• Solid understanding of data engineering principles and experience with tools for data processing and storage (e.g., Apache Spark, Hadoop, SQL databases, NoSQL databases).
• Familiarity with version control systems (e.g., Git) and continuous integration/continuous deployment (CI/CD) tools and practices.
• Security and networking background would be an advantage, with knowledge of security protocols, network architectures, firewalls, intrusion detection systems, and other relevant security and networking concepts.
• Strong problem-solving skills and ability to troubleshoot and resolve sophisticated issues in a timely manner.
• Excellent communication and collaboration skills, with the ability to work effectively in multi-functional teams.
• Attention to detail and a focus on quality, ensuring robustness and reliability in production ML systems.
  
  

• Exude high energy and a positive attitude.
• Stellar verbal and written communication skills.
• Passionate about data science and implementation.
• Have data science and GPU performance experience.
• Want to make what was impossible possible!