diff --git a/.github/workflows/e2e_tests.yaml b/.github/workflows/e2e_tests.yaml
index c925b6a52..0dcdbb992 100644
--- a/.github/workflows/e2e_tests.yaml
+++ b/.github/workflows/e2e_tests.yaml
@@ -18,7 +18,8 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  CODEFLARE_OPERATOR_IMG: "quay.io/project-codeflare/codeflare-operator:dev"
+  KUEUE_VERSION: v0.13.4
+  KUBERAY_VERSION: v1.4.2
 
 jobs:
   kubernetes:
@@ -37,23 +38,6 @@ jobs:
           ref: "main"
           path: "common"
 
-      - name: Checkout CodeFlare operator repository
-        uses: actions/checkout@v4
-        with:
-          repository: project-codeflare/codeflare-operator
-          path: codeflare-operator
-
-      - name: Set Go
-        uses: actions/setup-go@v5
-        with:
-          go-version-file: "./codeflare-operator/go.mod"
-          cache-dependency-path: "./codeflare-operator/go.sum"
-
-      - name: Set up gotestfmt
-        uses: gotesttools/gotestfmt-action@v2
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-
       - name: Set up specific Python version
         uses: actions/setup-python@v5
         with:
@@ -71,16 +55,55 @@ jobs:
       - name: Install NVidia GPU operator for KinD
         uses: ./common/github-actions/nvidia-gpu-operator
 
-      - name: Deploy CodeFlare stack
+      - name: Deploy Kueue and KubeRay
         id: deploy
         run: |
-          cd codeflare-operator
-          echo Setting up CodeFlare stack
-          make setup-e2e KUEUE_VERSION=v0.13.4 KUBERAY_VERSION=v1.4.0
-          echo Deploying CodeFlare operator
-          make deploy -e IMG="${CODEFLARE_OPERATOR_IMG}" -e ENV="e2e"
-          kubectl wait --timeout=120s --for=condition=Available=true deployment -n openshift-operators codeflare-operator-manager
-          cd ..
+          # Install Kueue
+          echo "Installing Kueue ${KUEUE_VERSION}..."
+          kubectl apply --server-side -f https://github.com/kubernetes-sigs/kueue/releases/download/${KUEUE_VERSION}/manifests.yaml
+          kubectl wait --timeout=120s --for=condition=Available=true deployment -n kueue-system kueue-controller-manager
+
+          # Install KubeRay from opendatahub-io fork (has RHOAI features)
+          echo "Installing KubeRay ${KUBERAY_VERSION} from opendatahub-io..."
+          kubectl create -k "github.com/opendatahub-io/kuberay/ray-operator/config/default?ref=${KUBERAY_VERSION}"
+          kubectl wait --timeout=120s --for=condition=Available=true deployment kuberay-operator
+
+          # Create default Kueue resources for the tests
+          echo "Creating Kueue resources..."
+          kubectl apply -f - <<EOF
+          apiVersion: kueue.x-k8s.io/v1beta1
+          kind: ResourceFlavor
+          metadata:
+            name: default-flavor
+          ---
+          apiVersion: kueue.x-k8s.io/v1beta1
+          kind: ClusterQueue
+          metadata:
+            name: cluster-queue
+          spec:
+            namespaceSelector: {}
+            resourceGroups:
+            - coveredResources: ["cpu", "memory", "nvidia.com/gpu"]
+              flavors:
+              - name: default-flavor
+                resources:
+                - name: cpu
+                  nominalQuota: 100
+                - name: memory
+                  nominalQuota: 100Gi
+                - name: nvidia.com/gpu
+                  nominalQuota: 10
+          ---
+          apiVersion: kueue.x-k8s.io/v1beta1
+          kind: LocalQueue
+          metadata:
+            name: local-queue
+            namespace: default
+            annotations:
+              kueue.x-k8s.io/default-queue: "true"
+          spec:
+            clusterQueue: cluster-queue
+          EOF
 
       - name: Add user to KinD
         uses: ./common/github-actions/kind-add-user
@@ -138,17 +161,11 @@ jobs:
           echo "Printing Pytest output logs"
           cat ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output.log
 
-      - name: Print CodeFlare operator logs
-        if: always() && steps.deploy.outcome == 'success'
-        run: |
-          echo "Printing CodeFlare operator logs"
-          kubectl logs -n openshift-operators --tail -1 -l app.kubernetes.io/name=codeflare-operator | tee ${CODEFLARE_TEST_OUTPUT_DIR}/codeflare-operator.log
-
       - name: Print KubeRay operator logs
         if: always() && steps.deploy.outcome == 'success'
         run: |
           echo "Printing KubeRay operator logs"
-          kubectl logs -n default --tail -1 -l app.kubernetes.io/name=kuberay | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kuberay.log
+          kubectl logs --tail -1 -l app.kubernetes.io/name=kuberay | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kuberay.log
 
       - name: Print Kueue controller logs
         if: always() && steps.deploy.outcome == 'success'
diff --git a/.github/workflows/rayjob_e2e_tests.yaml b/.github/workflows/rayjob_e2e_tests.yaml
index 757a66de8..5e46b2d25 100644
--- a/.github/workflows/rayjob_e2e_tests.yaml
+++ b/.github/workflows/rayjob_e2e_tests.yaml
@@ -18,7 +18,8 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  CODEFLARE_OPERATOR_IMG: "quay.io/project-codeflare/codeflare-operator:dev"
+  KUEUE_VERSION: v0.13.4
+  KUBERAY_VERSION: v1.4.2
 
 jobs:
   kubernetes-rayjob:
@@ -37,27 +38,10 @@ jobs:
           ref: "main"
           path: "common"
 
-      - name: Checkout CodeFlare operator repository
-        uses: actions/checkout@v4
-        with:
-          repository: project-codeflare/codeflare-operator
-          path: codeflare-operator
-
-      - name: Set Go
-        uses: actions/setup-go@v5
-        with:
-          go-version-file: "./codeflare-operator/go.mod"
-          cache-dependency-path: "./codeflare-operator/go.sum"
-
-      - name: Set up gotestfmt
-        uses: gotesttools/gotestfmt-action@v2
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-
       - name: Set up specific Python version
         uses: actions/setup-python@v5
         with:
-          python-version: "3.11"
+          python-version: "3.12"
           cache: "pip" # caching pip dependencies
 
       - name: Setup NVidia GPU environment for KinD
@@ -71,16 +55,55 @@ jobs:
       - name: Install NVidia GPU operator for KinD
         uses: ./common/github-actions/nvidia-gpu-operator
 
-      - name: Deploy CodeFlare stack
+      - name: Deploy Kueue and KubeRay
         id: deploy
         run: |
-          cd codeflare-operator
-          echo Setting up CodeFlare stack
-          make setup-e2e KUEUE_VERSION=v0.13.4 KUBERAY_VERSION=v1.4.0
-          echo Deploying CodeFlare operator
-          make deploy -e IMG="${CODEFLARE_OPERATOR_IMG}" -e ENV="e2e"
-          kubectl wait --timeout=120s --for=condition=Available=true deployment -n openshift-operators codeflare-operator-manager
-          cd ..
+          # Install Kueue
+          echo "Installing Kueue ${KUEUE_VERSION}..."
+          kubectl apply --server-side -f https://github.com/kubernetes-sigs/kueue/releases/download/${KUEUE_VERSION}/manifests.yaml
+          kubectl wait --timeout=120s --for=condition=Available=true deployment -n kueue-system kueue-controller-manager
+
+          # Install KubeRay from opendatahub-io fork (has RHOAI features)
+          echo "Installing KubeRay ${KUBERAY_VERSION} from opendatahub-io..."
+          kubectl create -k "github.com/opendatahub-io/kuberay/ray-operator/config/default?ref=${KUBERAY_VERSION}"
+          kubectl wait --timeout=120s --for=condition=Available=true deployment kuberay-operator
+
+          # Create default Kueue resources for the tests
+          echo "Creating Kueue resources..."
+          kubectl apply -f - <<EOF
+          apiVersion: kueue.x-k8s.io/v1beta1
+          kind: ResourceFlavor
+          metadata:
+            name: default-flavor
+          ---
+          apiVersion: kueue.x-k8s.io/v1beta1
+          kind: ClusterQueue
+          metadata:
+            name: cluster-queue
+          spec:
+            namespaceSelector: {}
+            resourceGroups:
+            - coveredResources: ["cpu", "memory", "nvidia.com/gpu"]
+              flavors:
+              - name: default-flavor
+                resources:
+                - name: cpu
+                  nominalQuota: 100
+                - name: memory
+                  nominalQuota: 100Gi
+                - name: nvidia.com/gpu
+                  nominalQuota: 10
+          ---
+          apiVersion: kueue.x-k8s.io/v1beta1
+          kind: LocalQueue
+          metadata:
+            name: local-queue
+            namespace: default
+            annotations:
+              kueue.x-k8s.io/default-queue: "true"
+          spec:
+            clusterQueue: cluster-queue
+          EOF
 
       - name: Add user to KinD
         uses: ./common/github-actions/kind-add-user
@@ -142,17 +165,11 @@ jobs:
           echo "Printing Pytest output logs"
           cat ${CODEFLARE_TEST_OUTPUT_DIR}/pytest_output_rayjob.log
 
-      - name: Print CodeFlare operator logs
-        if: always() && steps.deploy.outcome == 'success'
-        run: |
-          echo "Printing CodeFlare operator logs"
-          kubectl logs -n openshift-operators --tail -1 -l app.kubernetes.io/name=codeflare-operator | tee ${CODEFLARE_TEST_OUTPUT_DIR}/codeflare-operator.log
-
       - name: Print KubeRay operator logs
         if: always() && steps.deploy.outcome == 'success'
         run: |
           echo "Printing KubeRay operator logs"
-          kubectl logs -n default --tail -1 -l app.kubernetes.io/name=kuberay | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kuberay.log
+          kubectl logs --tail -1 -l app.kubernetes.io/name=kuberay | tee ${CODEFLARE_TEST_OUTPUT_DIR}/kuberay.log
 
       - name: Print Kueue controller logs
         if: always() && steps.deploy.outcome == 'success'
diff --git a/docs/designs/History/CodeFlareSDK_Design_Doc.md b/docs/designs/History/CodeFlareSDK_Design_Doc.md
index c7cb26fc0..257fc747b 100644
--- a/docs/designs/History/CodeFlareSDK_Design_Doc.md
+++ b/docs/designs/History/CodeFlareSDK_Design_Doc.md
@@ -97,7 +97,6 @@ We will rely on the Kubernetes cluster’s default security, where users cannot
     * System tests of SDK as part of the entire CodeFlare stack for main scenarios
 * Unit testing, integration testing, and system testing approaches
     * Unit testing will occur with every PR.
-    * For system testing we can leverage [current e2e](https://github.com/project-codeflare/codeflare-operator/tree/main/test/e2e) tests from the operator repo.
 * Validation criteria and expected outcomes
     * Minimum of 95% code coverage at all times.
     * Expect all unit tests to pass before a PR is merged.
diff --git a/docs/sphinx/user-docs/e2e.rst b/docs/sphinx/user-docs/e2e.rst
index c7a9e0558..4fc465635 100644
--- a/docs/sphinx/user-docs/e2e.rst
+++ b/docs/sphinx/user-docs/e2e.rst
@@ -4,7 +4,7 @@ Running e2e tests locally
 Pre-requisites
 ^^^^^^^^^^^^^^
 
--  We recommend using Python 3.11, along with Poetry.
+-  We recommend using Python 3.12, along with Poetry.
 
 On KinD clusters
 ----------------
@@ -21,18 +21,66 @@ instructions <https://www.substratus.ai/blog/kind-with-gpus>`__.
 
 -  Setup Phase:
 
-   -  Pull the `codeflare-operator
-      repo <https://github.com/project-codeflare/codeflare-operator>`__
-      and run the following make targets:
+   -  Create a KinD cluster:
 
    ::
 
-      make kind-e2e
-      export CLUSTER_HOSTNAME=kind
-      make setup-e2e
-      make deploy -e IMG=quay.io/project-codeflare/codeflare-operator:v1.3.0
+      kind create cluster
 
-      For running tests locally on Kind cluster, we need to disable `rayDashboardOAuthEnabled` in `codeflare-operator-config` ConfigMap and then restart CodeFlare Operator
+   -  Install Kueue:
+
+   ::
+
+      KUEUE_VERSION=v0.13.4
+      kubectl apply --server-side -f https://github.com/kubernetes-sigs/kueue/releases/download/${KUEUE_VERSION}/manifests.yaml
+      kubectl wait --timeout=120s --for=condition=Available=true deployment -n kueue-system kueue-controller-manager
+
+   -  Install KubeRay from the opendatahub-io fork (includes RHOAI features):
+
+   ::
+
+      KUBERAY_VERSION=v1.4.2
+      kubectl create -k "github.com/opendatahub-io/kuberay/ray-operator/config/default?ref=${KUBERAY_VERSION}"
+      kubectl wait --timeout=120s --for=condition=Available=true deployment kuberay-operator
+
+   -  Create Kueue resources (ResourceFlavor, ClusterQueue, LocalQueue):
+
+   ::
+
+      kubectl apply -f - <<EOF
+      apiVersion: kueue.x-k8s.io/v1beta1
+      kind: ResourceFlavor
+      metadata:
+        name: default-flavor
+      ---
+      apiVersion: kueue.x-k8s.io/v1beta1
+      kind: ClusterQueue
+      metadata:
+        name: cluster-queue
+      spec:
+        namespaceSelector: {}
+        resourceGroups:
+        - coveredResources: ["cpu", "memory", "nvidia.com/gpu"]
+          flavors:
+          - name: default-flavor
+            resources:
+            - name: cpu
+              nominalQuota: 100
+            - name: memory
+              nominalQuota: 100Gi
+            - name: nvidia.com/gpu
+              nominalQuota: 10
+      ---
+      apiVersion: kueue.x-k8s.io/v1beta1
+      kind: LocalQueue
+      metadata:
+        name: local-queue
+        namespace: default
+        annotations:
+          kueue.x-k8s.io/default-queue: "true"
+      spec:
+        clusterQueue: cluster-queue
+      EOF
 
    -  **(Optional)** - Create and add ``sdk-user`` with limited
       permissions to the cluster to run through the e2e tests:
@@ -68,21 +116,18 @@ instructions <https://www.substratus.ai/blog/kind-with-gpus>`__.
         kubectl create clusterrolebinding sdk-user-list-ingresses --clusterrole=list-ingresses --user=sdk-user
         kubectl create clusterrole namespace-creator --verb=get,list,create,delete,patch --resource=namespaces
         kubectl create clusterrolebinding sdk-user-namespace-creator --clusterrole=namespace-creator --user=sdk-user
-        kubectl create clusterrole list-rayclusters --verb=get,list --resource=rayclusters
-        kubectl create clusterrolebinding sdk-user-list-rayclusters --clusterrole=list-rayclusters --user=sdk-user
+        kubectl create clusterrole raycluster-creator --verb=get,list,create,delete,patch --resource=rayclusters
+        kubectl create clusterrolebinding sdk-user-raycluster-creator --clusterrole=raycluster-creator --user=sdk-user
+        kubectl create clusterrole rayjob-creator --verb=get,list,create,delete,patch --resource=rayjobs
+        kubectl create clusterrolebinding sdk-user-rayjob-creator --clusterrole=rayjob-creator --user=sdk-user
+        kubectl create clusterrole list-secrets --verb=get,list --resource=secrets
+        kubectl create clusterrolebinding sdk-user-list-secrets --clusterrole=list-secrets --user=sdk-user
         kubectl config use-context sdk-user
 
-   -  Install the latest development version of kueue
-
-   ::
-
-      kubectl apply --server-side -k "github.com/opendatahub-io/kueue/config/rhoai?ref=dev"
-
 -  Test Phase:
 
-   -  Once we have the codeflare-operator, kuberay-operator and kueue
-      running and ready, we can run the e2e test on the codeflare-sdk
-      repository:
+   -  Once we have kuberay-operator and kueue running and ready, we can
+      run the e2e test on the codeflare-sdk repository:
 
    ::
 
@@ -102,21 +147,60 @@ On OpenShift clusters
 
 -  Setup Phase:
 
-   -  Pull the `codeflare-operator
-      repo <https://github.com/project-codeflare/codeflare-operator>`__
-      and run the following make targets:
+   -  Install Kueue:
 
    ::
 
+      KUEUE_VERSION=v0.13.4
+      kubectl apply --server-side -f https://github.com/kubernetes-sigs/kueue/releases/download/${KUEUE_VERSION}/manifests.yaml
+      kubectl wait --timeout=120s --for=condition=Available=true deployment -n kueue-system kueue-controller-manager
+
+   -  Install KubeRay from the opendatahub-io fork (includes RHOAI features):
+
+   ::
 
-      make setup-e2e
-      make deploy -e IMG=quay.io/project-codeflare/codeflare-operator:v1.3.0
+      KUBERAY_VERSION=v1.4.2
+      kubectl create -k "github.com/opendatahub-io/kuberay/ray-operator/config/default?ref=${KUBERAY_VERSION}"
+      kubectl wait --timeout=120s --for=condition=Available=true deployment kuberay-operator
 
-   -  Install the latest development version of kueue
+   -  Create Kueue resources (ResourceFlavor, ClusterQueue, LocalQueue):
 
    ::
 
-      kubectl apply --server-side -k "github.com/opendatahub-io/kueue/config/rhoai?ref=dev"
+      kubectl apply -f - <<EOF
+      apiVersion: kueue.x-k8s.io/v1beta1
+      kind: ResourceFlavor
+      metadata:
+        name: default-flavor
+      ---
+      apiVersion: kueue.x-k8s.io/v1beta1
+      kind: ClusterQueue
+      metadata:
+        name: cluster-queue
+      spec:
+        namespaceSelector: {}
+        resourceGroups:
+        - coveredResources: ["cpu", "memory", "nvidia.com/gpu"]
+          flavors:
+          - name: default-flavor
+            resources:
+            - name: cpu
+              nominalQuota: 100
+            - name: memory
+              nominalQuota: 100Gi
+            - name: nvidia.com/gpu
+              nominalQuota: 10
+      ---
+      apiVersion: kueue.x-k8s.io/v1beta1
+      kind: LocalQueue
+      metadata:
+        name: local-queue
+        namespace: default
+        annotations:
+          kueue.x-k8s.io/default-queue: "true"
+      spec:
+        clusterQueue: cluster-queue
+      EOF
 
 If the system you run on contains NVidia GPU then you can enable the GPU
 support on OpenShift, this will allow you to run also GPU tests. To
@@ -127,9 +211,8 @@ executed on nodes with taint (i.e. GPU taint).
 
 -  Test Phase:
 
-   -  Once we have the codeflare-operator, kuberay-operator and kueue
-      running and ready, we can run the e2e test on the codeflare-sdk
-      repository:
+   -  Once we have kuberay-operator and kueue running and ready, we can
+      run the e2e test on the codeflare-sdk repository:
 
    ::
 
diff --git a/tests/e2e/local_interactive_sdk_kind_test.py b/tests/e2e/local_interactive_sdk_kind_test.py
index 1dd8a2e02..2f9b14a01 100644
--- a/tests/e2e/local_interactive_sdk_kind_test.py
+++ b/tests/e2e/local_interactive_sdk_kind_test.py
@@ -67,11 +67,18 @@ def run_local_interactives(
 
         cluster.apply()
 
-        cluster.wait_ready()
+        # Disable dashboard check on KinD as HTTPRoute/Route is not available
+        cluster.wait_ready(dashboard_check=False)
         cluster.status()
 
-        generate_cert.generate_tls_cert(cluster_name, self.namespace)
-        generate_cert.export_env(cluster_name, self.namespace)
+        # Try to generate TLS certs, but don't fail if CA secret is not available
+        # (CA secret is created by codeflare-operator which is not deployed on KinD)
+        try:
+            generate_cert.generate_tls_cert(cluster_name, self.namespace)
+            generate_cert.export_env(cluster_name, self.namespace)
+        except Exception as e:
+            print(f"Warning: Could not generate TLS certificates: {e}")
+            print("Continuing without TLS - using port-forwarding for local connection")
 
         print(cluster.local_client_url())
 
diff --git a/tests/e2e/mnist_raycluster_sdk_kind_test.py b/tests/e2e/mnist_raycluster_sdk_kind_test.py
index 4ba728cfd..ae3d21e30 100644
--- a/tests/e2e/mnist_raycluster_sdk_kind_test.py
+++ b/tests/e2e/mnist_raycluster_sdk_kind_test.py
@@ -1,4 +1,5 @@
 import requests
+import subprocess
 
 from time import sleep
 
@@ -16,8 +17,16 @@
 class TestRayClusterSDKKind:
     def setup_method(self):
         initialize_kubernetes_client(self)
+        self.port_forward_process = None
+
+    def cleanup_port_forward(self):
+        if self.port_forward_process:
+            self.port_forward_process.terminate()
+            self.port_forward_process.wait(timeout=10)
+            self.port_forward_process = None
 
     def teardown_method(self):
+        self.cleanup_port_forward()
         delete_namespace(self)
         delete_kueue_resources(self)
 
@@ -58,7 +67,8 @@ def run_mnist_raycluster_sdk_kind(
 
         cluster.status()
 
-        cluster.wait_ready()
+        # Disable dashboard check on KinD as HTTPRoute/Route is not available
+        cluster.wait_ready(dashboard_check=False)
 
         cluster.status()
 
@@ -66,46 +76,74 @@ def run_mnist_raycluster_sdk_kind(
 
         self.assert_jobsubmit_withoutlogin_kind(cluster, accelerator, number_of_gpus)
 
-        assert_get_cluster_and_jobsubmit(
-            self, "mnist", accelerator="gpu", number_of_gpus=1
-        )
+        # Note: assert_get_cluster_and_jobsubmit uses cluster.job_client which requires
+        # the dashboard URL (HTTPRoute/Route). Since this is not available on KinD,
+        # we skip this call. Job submission is already tested above with port-forwarding.
+        # This function is tested on OpenShift in mnist_raycluster_sdk_test.py
+
+        cluster.down()
 
     # Assertions
 
     def assert_jobsubmit_withoutlogin_kind(self, cluster, accelerator, number_of_gpus):
-        ray_dashboard = cluster.cluster_dashboard_uri()
+        # Use port-forwarding to access the dashboard since HTTPRoute/Route is not available on KinD
+        local_port = "8265"
+        dashboard_port = "8265"
+        cluster_name = cluster.config.name
+
+        port_forward_cmd = [
+            "kubectl",
+            "port-forward",
+            "-n",
+            self.namespace,
+            f"svc/{cluster_name}-head-svc",
+            f"{local_port}:{dashboard_port}",
+        ]
+        self.port_forward_process = subprocess.Popen(
+            port_forward_cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL
+        )
+
+        # Wait for port-forward to be ready
+        sleep(5)
+
+        ray_dashboard = f"http://localhost:{local_port}"
         client = RayJobClient(address=ray_dashboard, verify=False)
 
-        submission_id = client.submit_job(
-            entrypoint="python mnist.py",
-            runtime_env={
-                "working_dir": "./tests/e2e/",
-                "pip": "./tests/e2e/mnist_pip_requirements.txt",
-                "env_vars": get_setup_env_variables(ACCELERATOR=accelerator),
-            },
-            entrypoint_num_gpus=number_of_gpus,
-        )
-        print(f"Submitted job with ID: {submission_id}")
-        done = False
-        time = 0
-        timeout = 900
-        while not done:
-            status = client.get_job_status(submission_id)
-            if status.is_terminal():
-                break
-            if not done:
-                print(status)
-                if timeout and time >= timeout:
-                    raise TimeoutError(f"job has timed out after waiting {timeout}s")
-                sleep(5)
-                time += 5
-
-        logs = client.get_job_logs(submission_id)
-        print(logs)
-
-        self.assert_job_completion(status)
-
-        client.delete_job(submission_id)
+        try:
+            submission_id = client.submit_job(
+                entrypoint="python mnist.py",
+                runtime_env={
+                    "working_dir": "./tests/e2e/",
+                    "pip": "./tests/e2e/mnist_pip_requirements.txt",
+                    "env_vars": get_setup_env_variables(ACCELERATOR=accelerator),
+                },
+                entrypoint_num_gpus=number_of_gpus,
+            )
+            print(f"Submitted job with ID: {submission_id}")
+            done = False
+            time = 0
+            timeout = 900
+            while not done:
+                status = client.get_job_status(submission_id)
+                if status.is_terminal():
+                    break
+                if not done:
+                    print(status)
+                    if timeout and time >= timeout:
+                        raise TimeoutError(
+                            f"job has timed out after waiting {timeout}s"
+                        )
+                    sleep(5)
+                    time += 5
+
+            logs = client.get_job_logs(submission_id)
+            print(logs)
+
+            self.assert_job_completion(status)
+
+            client.delete_job(submission_id)
+        finally:
+            self.cleanup_port_forward()
 
     def assert_job_completion(self, status):
         if status == "SUCCEEDED":
diff --git a/tests/e2e/rayjob/rayjob_existing_cluster_test.py b/tests/e2e/rayjob/rayjob_existing_cluster_test.py
index 00faa2d9e..8acdc1c5f 100644
--- a/tests/e2e/rayjob/rayjob_existing_cluster_test.py
+++ b/tests/e2e/rayjob/rayjob_existing_cluster_test.py
@@ -67,8 +67,9 @@ def test_existing_kueue_cluster(self):
         cluster.apply()
 
         # Wait for cluster to be ready (with Kueue admission)
+        # On KinD, disable dashboard check as HTTPRoute/Route is not available
         print(f"Waiting for cluster '{cluster_name}' to be ready...")
-        cluster.wait_ready(timeout=600)
+        cluster.wait_ready(timeout=600, dashboard_check=is_openshift())
         print(f"✓ Cluster '{cluster_name}' is ready")
 
         # RayJob with explicit local_queue (will be ignored for existing clusters)