Automating pallet handling in outdoor logistics and construction environments remains challenging due to unstructured scenes, variable pallet configurations, and changing environmental conditions. In this paper, we present Lang2Lift, an end-to-end language-guided autonomous forklift system designed to support practical pallet pick-up operations in real-world outdoor settings. The system enables operators to specify target pallets using natural language instructions, allowing flexible selection among multiple pallets with different loads and spatial arrangements. Lang2Lift integrates foundation-model-based perception modules with motion planning and control in a closed-loop autonomy pipeline. Language-grounded visual perception is used to identify and segment target pallets, followed by 6D pose estimation and geometric refinement to generate manipulation-feasible insertion poses. The resulting pose estimates are directly coupled with the forklift planning and control modules to execute fully autonomous pallet pick-up maneuvers. We deploy and evaluate the proposed system on the ADAPT autonomous outdoor forklift platform across diverse real-world scenarios, including cluttered scenes, variable lighting, and different payload configurations. Tolerance-based pose evaluation further indicates accuracy sufficient for successful fork insertion. Timing and failure analyses highlight key deployment trade-offs and practical limitations, providing insights into integrating language-guided perception within industrial automation systems. Video demonstrations are available at https://eric-nguyen1402.github.io/lang2lift.github.io/