senior-ml-engineer

To deploy a PyTorch model to a Kubernetes cluster, you need a productionization pipeline that packages the model and configures the infrastructure. This involves building scalable ML systems and using MLOps practices to manage the deployment effectively.

Monitoring ML models for drift in production requires implementing robust MLOps infrastructure. You can set up continuous monitoring pipelines to track model performance and detect data or concept drift over time within your deployed ML systems.

Yes, you can integrate RAG systems and fine-tune LLMs using these ML engineering capabilities. It supports building production-grade AI systems, allowing you to implement and scale advanced LLM integrations and agentic AI workflows.

Yes, building scalable ML platforms requires robust MLOps infrastructure. You need to establish automated pipelines for model deployment, feature stores, and continuous monitoring to effectively manage and scale your production ML systems.

Feature stores provide a centralized repository for serving and managing ML features during model productionization. They integrate into your MLOps pipelines to ensure consistent feature computation and availability across training and deployment environments.

Building ML infrastructure for agentic AI requires managing complex dependencies and scalable systems. Limitations often involve maintaining low-latency model deployment, ensuring robust LLM integration, and handling continuous monitoring for dynamic agentic workflows.