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

• 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

•  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