Kubernetes

Role Overview
We are seeking a highly skilled Senior MLOps Engineer with 8+ years of experience to join our team. The ideal candidate will have extensive expertise in model deployment, model monitoring, and productionizing machine learning models. You will play a crucial role in designing and implementing efficient workflows for AI programming and team communication, ensuring seamless integration of ML solutions within our organization.
Key Responsibilities:
• Workflow Design & Implementation: Oversee the implementation of workflows for AI programming and team communication, ensuring optimal collaboration and efficiency.
• Model Deployment: Manage and optimize model deployment processes, including the use of Kubernetes for containerized model deployment and orchestration.
• Model Registry Management: Maintain and manage a model registry to track versions and ensure smooth transitions from development to production.
• CI/CD Implementation: Develop and implement Continuous Integration/Continuous Deployment (CI/CD) pipelines for model training, testing, and deployment, ensuring high code quality through rigorous model code reviews.
• Model Monitoring & Optimization: Design and implement model inference pipelines and monitoring frameworks to support thousands of models across various pods, optimizing execution times and resource usage.
• Team Leadership & Training: Manage, mentor, and train junior engineers, fostering their growth and learning while overseeing a large team
• Collaboration with Data Science Teams: Train and collaborate with data science team members on best practices in tools such as Kubeflow, Jenkins, Docker, and Kubernetes to ensure smooth model productionization.
• Reusable Frameworks Development: Draft designs and apply reusable frameworks for drift detection, live inference, and API integration.
• Cost Optimization Initiatives: Propose and implement strategies to reduce operational costs, including optimizing models for resource efficiency, resulting in significant annual savings.
• Documentation & Standards Development: Produce MLE standards documents to assist data science teams in deploying their models effectively and consistently.
Qualifications:
• 8+ years of experience in MLOps, model deployment, and productionizing machine learning models.
• Proficient in Kubernetes, model monitoring, and CI/CD practices. Experience working in the Azure environment.
• Strong understanding of model registry concepts and best practices.
• Experience with programming languages and ML frameworks (e.g., TensorFlow, PyTorch).
• Proven track record of optimizing ML workflows and processes.
• Excellent communication and leadership skills, with experience in mentoring and training team members.
• Ability to work in a fast-paced, collaborative environment.