Ml/Ops Engineer

Overview:

Our client is looking for a talented and experienced ML/Ops Engineer to play a crucial role in administering and scaling their machine learning platform in production environments. This person will collaborate closely with data scientists, software engineers, and DevOps professionals to ensure the seamless administration and scalability of machine learning solutions.

Responsibilities:

• Administer and scale machine learning models, ensuring reliability and efficiency. Work with software engineers to integrate models into existing applications and systems.

• Design and maintain infrastructure for machine learning workflows, including data storage, processing, and model serving. Implement and manage cloud-based services and orchestration tools for ML deployments.

• Develop automation scripts and tools to streamline the deployment and monitoring of machine learning pipelines. Implement and scale robust monitoring and alerting systems to detect and respond to issues in real-time.

• Optimize machine learning workflows for performance, scalability, and cost-effectiveness. Conduct experiments and performance tuning to improve model inference speed and resource utilization.

• Ensure the security and compliance of machine learning systems by creating best practices for data privacy, access control, and auditability. Collaborate with security teams to identify and mitigate potential risks.

• Establish and maintain comprehensive documentation for ML infrastructure, processes, and best practices. Communicate knowledge and insights to broader teams and stakeholders.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.

• Proven experience in deploying, administering, and scaling machine learning models/platforms in production environments.

• Proficiency in programming languages such as Python, Java, or Scala.

• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud.

• Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes.

• Strong understanding of DevOps principles and practices.

• Experience with tools such as Voila, Comet, and Fiddler

• C3 AI platform experience

• Knowledge of security best practices for machine learning systems.

• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.