What you will do:

• ML Workflow Management: Design, implement, and optimize ML workflows using Kubeflow and MLflow to streamline the development, training, and deployment of machine learning models.
• Model Deployment: Deploy ML models into production environments using Kubeflow pipelines, ensuring scalability, reliability, and performance.
• LLM Management: Implement and manage large language models (LLMs) for natural language processing tasks, including training, fine-tuning, and inference.
• Infrastructure Orchestration: Utilize Kubernetes for container orchestration and manage scalable, fault-tolerant infrastructure for ML workloads.
• Automation: Develop automation scripts and tools to automate deployment, monitoring, and maintenance tasks, minimizing manual intervention and improving efficiency.
• Version Control: Implement version control for ML models, datasets, and experiments using Git and MLflow, ensuring reproducibility and traceability.
• Monitoring and Logging: Set up monitoring and logging systems to track the performance and health of ML workflows and infrastructure components, and implement alerts for proactive issue resolution.
• Security and Compliance: Implement security best practices and compliance standards for sensitive data handling in ML workflows, ensuring adherence to privacy regulations.
• Collaboration: Collaborate closely with data scientists, software engineers, and other stakeholders to integrate ML models into production systems effectively.
• Documentation: Document deployment processes, infrastructure configurations, and troubleshooting procedures to facilitate knowledge sharing and ensure system reliability.

What makes you a great fit:

• Proven experience in deploying and managing ML workflows using Kubeflow, MLflow, or similar tools in production environments.
  
• Strong programming skills in Python, with experience in developing and maintaining production-grade code.
• Proficiency in containerization technologies such as Docker and container orchestration tools like Kubernetes.
• Experience with large language model management systems for natural language processing tasks, such as Hugging Face Transformers.
• Familiarity with DevOps practices and tools, including CI/CD pipelines, infrastructure as code, and configuration management.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment.
• Experience with version control systems such as Git and familiarity with GitOps principles.
• Knowledge of security best practices and data privacy regulations.
• Ability to thrive in a fast-paced, dynamic environment and adapt to changing priorities.