• Gain a deep understanding of the three pillars of observability (logs, metrics, traces) and how they are used to monitor complex systems. • Help with validating, documenting, and exploring new data sources as they are onboarded into our observability platforms. • Assist senior engineers in building and refining dashboards that provide clear insights into system health and performance. • Assist data scientists with tasks related to model creation, implementation, and deployment, such as data preparation, feature exploration, and validating model results for observability use cases. • Learn how AI/LLM applications are monitored by assisting with the instrumentation and analysis of telemetry from tools like Langfuse. • Help write and refine simple scripts (primarily in Python) to automate data quality checks or reporting tasks. • Help create and update documentation for tools, processes, and team projects.