About your tasks:

• Develop and maintain CI/CD pipelines for deploying ML and LLM models in production, ensuring rapid and reliable model iteration.
• Optimize the deployment process for LLMs, considering cost, latency, and scalability constraints.
• Automate model retraining and redeployment processes to respond to changing data patterns.
• Set up and manage cloud and on-premise infrastructure for ML/LLM workloads on platforms such as AWS, GCP, or Azure.
• Work with containerization and orchestration tools (e.g., Docker, Kubernetes) to ensure scalable and resilient model deployment.
• Implement data versioning and validation processes to maintain data integrity throughout the ML lifecycle.
• Use tools like MLflow for tracking experiments, managing model versions, and maintaining reproducibility.
• Document MLOps/LLMOps practices and mentor team members on deployment best practices.