Responsibilities:

• Design, implement, and manage scalable MLOps infrastructure for machine learning workflows
• Deploy and manage Kubernetes clusters to support machine learning environments
• Set up and maintain platforms like JupyterHub, Airflow, and Weights & Biases (W&B) for ML experiment tracking and orchestration
• Develop monitoring solutions to track system performance, model training, and deployment pipelines
• Automate the deployment, scaling, and monitoring of machine learning models and services
• Work with GPU-based environments to enable high-performance training and inference
• Collaborate with data science and engineering teams to integrate MLOps tools into existing workflows
• Troubleshoot and resolve issues related to infrastructure, pipelines, and model deployments
• Continuously improve and optimize the infrastructure to meet the demands of scaling machine learning models

Requirements:

• Strong hands-on experience with Kubernetes for managing containerized ML environments
• Experience in deploying and managing platforms like JupyterHub for collaborative data science work
• Familiarity with Airflow for orchestrating ML pipelines
• Proficiency with Weights & Biases (W&B) for experiment tracking and model versioning
• Experience with monitoring and logging tools to ensure the reliability and performance of ML systems
• Ability to work with GPUs for accelerating model training and inference
• Strong understanding of cloud infrastructure and automation tools (Terraform, Ansible, etc.)
• Familiarity with CI/CD for ML model deployment and automation
• Excellent communication skills and the ability to work collaboratively with cross-functional teams

Bonus Points:
Experience with distributed training systems or ML frameworks like TensorFlow, PyTorch
Familiarity with model serving frameworks like TensorFlow Serving or KFServing
Knowledge of security best practices in ML pipelines and model deployment