Description

As an MLOps Engineer, you will design and optimize pipelines for machine learning models, ensuring their smooth transition from development to production. This role involves collaborating with teams, monitoring model performance, and sharing best practices to enhance efficiency.

Requirements

• •Proficiency in modern DevOps practices and microservice architecture
• •Expertise in Kubernetes and containerization technologies
• •Hands-on experience with KubeFlow, Kserve, or equivalent
• •Experience in ML Experimentation and registry platforms such as W&B or MLFLow
• •Understanding of time series modeling and its data requirements
• •Familiarity with ML/NN frameworks
• •Familiarity with AWS or other cloud service providers
• •Strong collaboration skills with cross-functional teams
• •Clear and concise verbal and written communication
• •Fluency in written and spoken English; German is a plus

Tasks

• •Design and maintain scalable pipelines for deploying machine learning models in-cloud or in-vehicle
• •Ensure secure integration of models into production environments with minimal latency
• •Implement monitoring systems to track model performance and flag issues
• •Develop methods to evaluate and compare the performance of different models
• •Automate processes for validating model accuracy and consistency in production
• •Collaborate with data scientists, developers, and stakeholders to understand their needs
• •Communicate technical processes and outcomes to technical and non-technical audiences
• •Create comprehensive documentation for processes, pipelines, and workflows
• •Provide training and guidance on MLOps best practices

Tools & Technologies