diff --git a/keras_cv/layers/preprocessing/random_shear.py b/keras_cv/layers/preprocessing/random_shear.py
index 9567cb65ca..0696f10ee0 100644
--- a/keras_cv/layers/preprocessing/random_shear.py
+++ b/keras_cv/layers/preprocessing/random_shear.py
@@ -143,23 +143,9 @@ def augment_ragged_image(self, image, transformation, **kwargs):
 
     def augment_images(self, images, transformations, **kwargs):
         x, y = transformations["shear_x"], transformations["shear_y"]
-        batch_size = tf.shape(images)[0]
 
-        base_transforms = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]
-        base_transforms = tf.repeat(
-            [base_transforms], repeats=batch_size, axis=0
-        )
         if x is not None:
-            # insert x into the 2nd column of base_transforms
-            # aka stack N vectors of [1.0, x, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]
-            transforms_x = tf.concat(
-                [
-                    tf.expand_dims(base_transforms[:, 0], axis=-1),
-                    x,
-                    base_transforms[:, 2:],
-                ],
-                axis=-1,
-            )
+            transforms_x = self._build_shear_x_transform_matrix(x)
             images = preprocessing.transform(
                 images=images,
                 transforms=transforms_x,
@@ -169,12 +155,7 @@ def augment_images(self, images, transformations, **kwargs):
             )
 
         if y is not None:
-            # insert y into the 4th column of base_transforms
-            # aka stack N vectors of [1.0, 0.0, 0.0, y, 1.0, 0.0, 0.0, 0.0]
-            transforms_y = tf.concat(
-                [base_transforms[:, :3], y, base_transforms[:, 4:]], axis=-1
-            )
-
+            transforms_y = self._build_shear_y_transform_matrix(y)
             images = preprocessing.transform(
                 images=images,
                 transforms=transforms_y,
@@ -185,6 +166,56 @@ def augment_images(self, images, transformations, **kwargs):
 
         return images
 
+    @staticmethod
+    def _build_shear_x_transform_matrix(shear_x):
+        """Build transform matrix for horizontal shear.
+
+        The transform matrix looks like:
+        (1, x, 0)
+        (0, 1, 0)
+        (0, 0, 1)
+        where the last entry is implicit.
+
+        We flatten the matrix to `[1.0, x, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]` for
+        use with ImageProjectiveTransformV3.
+        """
+        batch_size = tf.shape(shear_x)[0]
+        return tf.concat(
+            values=[
+                tf.ones((batch_size, 1), tf.float32),
+                shear_x,
+                tf.zeros((batch_size, 2), tf.float32),
+                tf.ones((batch_size, 1), tf.float32),
+                tf.zeros((batch_size, 3), tf.float32),
+            ],
+            axis=1,
+        )
+
+    @staticmethod
+    def _build_shear_y_transform_matrix(shear_y):
+        """Build transform matrix for vertical shear.
+
+        The transform matrix looks like:
+        (1, 0, 0)
+        (y, 1, 0)
+        (0, 0, 1)
+        where the last entry is implicit.
+
+        We flatten the matrix to `[1.0, 0.0, 0.0, y, 1.0, 0.0, 0.0, 0.0]` for
+        use ImageProjectiveTransformV3.
+        """
+        batch_size = tf.shape(shear_y)[0]
+        return tf.concat(
+            values=[
+                tf.ones((batch_size, 1), tf.float32),
+                tf.zeros((batch_size, 2), tf.float32),
+                shear_y,
+                tf.ones((batch_size, 1), tf.float32),
+                tf.zeros((batch_size, 3), tf.float32),
+            ],
+            axis=1,
+        )
+
     def augment_labels(self, labels, transformations, **kwargs):
         return labels