diff --git a/poetry.lock b/poetry.lock
index 89bca28..fca5583 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -14,103 +14,103 @@ files = [
 
 [[package]]
 name = "aiohttp"
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+version = "3.12.14"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
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 ]
 
 [package.dependencies]
 aiohappyeyeballs = ">=2.5.0"
-aiosignal = ">=1.1.2"
+aiosignal = ">=1.4.0"
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
@@ -137,18 +137,19 @@ aiohttp = ">=3.9"
 
 [[package]]
 name = "aiosignal"
-version = "1.3.2"
+version = "1.4.0"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
 frozenlist = ">=1.1.0"
+typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
 
 [[package]]
 name = "alabaster"
@@ -454,14 +455,14 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2025.4.26"
+version = "2025.7.9"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 groups = ["main", "docs", "test"]
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 [[package]]
@@ -2321,75 +2323,75 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 
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-version = "3.2.2"
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 description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
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 [package.extras]
@@ -2397,6 +2399,26 @@ rsa = ["cryptography (>=3.0.0)"]
 signals = ["blinker (>=1.4.0)"]
 signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
+[[package]]
+name = "odh-kuberay-client"
+version = "0.0.0.dev40"
+description = "Python SDK for Kuberay client"
+optional = false
+python-versions = ">=3.11,<4.0"
+groups = ["main"]
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+]
+
+[package.dependencies]
+kubernetes = ">=25.0.0"
+
+[package.source]
+type = "legacy"
+url = "https://test.pypi.org/simple"
+reference = "testpypi"
+
 [[package]]
 name = "opencensus"
 version = "0.11.4"
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+reference = "testpypi"
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 [package.dependencies]
@@ -3749,129 +3762,156 @@ jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
 [[package]]
 name = "rpds-py"
-version = "0.25.1"
+version = "0.26.0"
 description = "Python bindings to Rust's persistent data structures (rpds)"
 optional = false
 python-versions = ">=3.9"
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+    {file = "rpds_py-0.26.0-pp39-pypy39_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:b5f7a446ddaf6ca0fad9a5535b56fbfc29998bf0e0b450d174bbec0d600e1d72"},
+    {file = "rpds_py-0.26.0-pp39-pypy39_pp73-musllinux_1_2_i686.whl", hash = "sha256:eed5ac260dd545fbc20da5f4f15e7efe36a55e0e7cf706e4ec005b491a9546a0"},
+    {file = "rpds_py-0.26.0-pp39-pypy39_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:582462833ba7cee52e968b0341b85e392ae53d44c0f9af6a5927c80e539a8b67"},
+    {file = "rpds_py-0.26.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:69a607203441e07e9a8a529cff1d5b73f6a160f22db1097211e6212a68567d11"},
+    {file = "rpds_py-0.26.0.tar.gz", hash = "sha256:20dae58a859b0906f0685642e591056f1e787f3a8b39c8e8749a45dc7d26bdb0"},
 ]
 
 [[package]]
@@ -3941,27 +3981,27 @@ files = [
 
 [[package]]
 name = "smart-open"
-version = "7.1.0"
-description = "Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)"
+version = "7.3.0.post1"
+description = "Utils for streaming large files (S3, HDFS, GCS, SFTP, Azure Blob Storage, gzip, bz2, zst...)"
 optional = false
-python-versions = "<4.0,>=3.7"
+python-versions = "<4.0,>=3.8"
 groups = ["main"]
 files = [
-    {file = "smart_open-7.1.0-py3-none-any.whl", hash = "sha256:4b8489bb6058196258bafe901730c7db0dcf4f083f316e97269c66f45502055b"},
-    {file = "smart_open-7.1.0.tar.gz", hash = "sha256:a4f09f84f0f6d3637c6543aca7b5487438877a21360e7368ccf1f704789752ba"},
+    {file = "smart_open-7.3.0.post1-py3-none-any.whl", hash = "sha256:c73661a2c24bf045c1e04e08fffc585b59af023fe783d57896f590489db66fb4"},
+    {file = "smart_open-7.3.0.post1.tar.gz", hash = "sha256:ce6a3d9bc1afbf6234ad13c010b77f8cd36d24636811e3c52c3b5160f5214d1e"},
 ]
 
 [package.dependencies]
 wrapt = "*"
 
 [package.extras]
-all = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "paramiko", "requests", "zstandard"]
+all = ["smart_open[azure,gcs,http,s3,ssh,webhdfs,zst]"]
 azure = ["azure-common", "azure-core", "azure-storage-blob"]
 gcs = ["google-cloud-storage (>=2.6.0)"]
 http = ["requests"]
 s3 = ["boto3"]
 ssh = ["paramiko"]
-test = ["awscli", "azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "moto[server]", "numpy", "paramiko", "pyopenssl", "pytest", "pytest-benchmark", "pytest-rerunfailures", "requests", "responses", "zstandard"]
+test = ["awscli", "moto[server]", "numpy", "pyopenssl", "pytest", "pytest-rerunfailures", "pytest_benchmark", "responses", "smart_open[all]"]
 webhdfs = ["requests"]
 zst = ["zstandard"]
 
@@ -4272,26 +4312,26 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.9.0.20250516"
+version = "2.9.0.20250708"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.9"
 groups = ["test"]
 files = [
-    {file = "types_python_dateutil-2.9.0.20250516-py3-none-any.whl", hash = "sha256:2b2b3f57f9c6a61fba26a9c0ffb9ea5681c9b83e69cd897c6b5f668d9c0cab93"},
-    {file = "types_python_dateutil-2.9.0.20250516.tar.gz", hash = "sha256:13e80d6c9c47df23ad773d54b2826bd52dbbb41be87c3f339381c1700ad21ee5"},
+    {file = "types_python_dateutil-2.9.0.20250708-py3-none-any.whl", hash = "sha256:4d6d0cc1cc4d24a2dc3816024e502564094497b713f7befda4d5bc7a8e3fd21f"},
+    {file = "types_python_dateutil-2.9.0.20250708.tar.gz", hash = "sha256:ccdbd75dab2d6c9696c350579f34cffe2c281e4c5f27a585b2a2438dd1d5c8ab"},
 ]
 
 [[package]]
 name = "typing-extensions"
-version = "4.14.0"
+version = "4.14.1"
 description = "Backported and Experimental Type Hints for Python 3.9+"
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "test"]
 files = [
-    {file = "typing_extensions-4.14.0-py3-none-any.whl", hash = "sha256:a1514509136dd0b477638fc68d6a91497af5076466ad0fa6c338e44e359944af"},
-    {file = "typing_extensions-4.14.0.tar.gz", hash = "sha256:8676b788e32f02ab42d9e7c61324048ae4c6d844a399eebace3d4979d75ceef4"},
+    {file = "typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76"},
+    {file = "typing_extensions-4.14.1.tar.gz", hash = "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36"},
 ]
 
 [[package]]
@@ -4323,14 +4363,14 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.4.0"
+version = "2.5.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "docs", "test"]
 files = [
-    {file = "urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813"},
-    {file = "urllib3-2.4.0.tar.gz", hash = "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466"},
+    {file = "urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"},
+    {file = "urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760"},
 ]
 
 [package.extras]
@@ -4656,4 +4696,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.11"
-content-hash = "29175d71269b0c8a1a53eb7027e8d824ef150bf8b4aa4cea8a9ed75684aa6e43"
+content-hash = "a308f6517e3e8c5c14542421019a82056e8a81b43fb316a7862503ad799b9d49"
diff --git a/pyproject.toml b/pyproject.toml
index 8615f89..fe1ae40 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,6 +29,14 @@ cryptography = "43.0.3"
 executing = "1.2.0"
 pydantic = "< 2"
 ipywidgets = "8.1.2"
+odh-kuberay-client = {version = "0.0.0.dev40", source = "testpypi"}
+
+[[tool.poetry.source]]
+name = "pypi"
+
+[[tool.poetry.source]]
+name = "testpypi"
+url = "https://test.pypi.org/simple/"
 
 [tool.poetry.group.docs]
 optional = true
diff --git a/src/codeflare_sdk/__init__.py b/src/codeflare_sdk/__init__.py
index 9ab5c74..95753a5 100644
--- a/src/codeflare_sdk/__init__.py
+++ b/src/codeflare_sdk/__init__.py
@@ -10,6 +10,7 @@
     AWManager,
     AppWrapperStatus,
     RayJobClient,
+    RayJob,
 )
 
 from .common.widgets import view_clusters
diff --git a/src/codeflare_sdk/ray/__init__.py b/src/codeflare_sdk/ray/__init__.py
index ab55cc8..7c8e84d 100644
--- a/src/codeflare_sdk/ray/__init__.py
+++ b/src/codeflare_sdk/ray/__init__.py
@@ -4,6 +4,10 @@
     RayJobClient,
 )
 
+from .rayjobs import (
+    RayJob,
+)
+
 from .cluster import (
     Cluster,
     ClusterConfiguration,
diff --git a/src/codeflare_sdk/ray/cluster/build_ray_cluster.py b/src/codeflare_sdk/ray/cluster/build_ray_cluster.py
index 92cefef..9d7ed10 100644
--- a/src/codeflare_sdk/ray/cluster/build_ray_cluster.py
+++ b/src/codeflare_sdk/ray/cluster/build_ray_cluster.py
@@ -136,6 +136,7 @@ def build_ray_cluster(cluster: "codeflare_sdk.ray.cluster.Cluster"):
                 "enableIngress": False,
                 "rayStartParams": {
                     "dashboard-host": "0.0.0.0",
+                    "dashboard-port": "8265",
                     "block": "true",
                     "num-gpus": str(head_gpu_count),
                     "resources": head_resources,
@@ -245,6 +246,7 @@ def get_labels(cluster: "codeflare_sdk.ray.cluster.Cluster"):
     """
     labels = {
         "controller-tools.k8s.io": "1.0",
+        "ray.io/cluster": cluster.config.name,  # Enforced label always present
     }
     if cluster.config.labels != {}:
         labels.update(cluster.config.labels)
diff --git a/src/codeflare_sdk/ray/cluster/cluster.py b/src/codeflare_sdk/ray/cluster/cluster.py
index 86ed7c4..2e3d14d 100644
--- a/src/codeflare_sdk/ray/cluster/cluster.py
+++ b/src/codeflare_sdk/ray/cluster/cluster.py
@@ -20,8 +20,14 @@
 
 from time import sleep
 from typing import List, Optional, Tuple, Dict
+import copy
 
-from ray.job_submission import JobSubmissionClient
+from ray.job_submission import JobSubmissionClient, JobStatus
+import time
+import uuid
+import warnings
+
+from ..job.job import RayJobSpec
 
 from ...common.kubernetes_cluster.auth import (
     config_check,
@@ -57,7 +63,6 @@
 from kubernetes.client.rest import ApiException
 
 from kubernetes.client.rest import ApiException
-import warnings
 
 CF_SDK_FIELD_MANAGER = "codeflare-sdk"
 
@@ -604,6 +609,298 @@ def _component_resources_down(
             yamls = yaml.safe_load_all(self.resource_yaml)
             _delete_resources(yamls, namespace, api_instance, cluster_name)
 
+    @staticmethod
+    def run_job_with_managed_cluster(
+        cluster_config: ClusterConfiguration,
+        job_config: RayJobSpec,
+        job_cr_name: Optional[str] = None,
+        submission_mode: str = "K8sJobMode",
+        shutdown_after_job_finishes: bool = True,
+        ttl_seconds_after_finished: Optional[int] = None,
+        suspend_rayjob_creation: bool = False,
+        wait_for_completion: bool = True,
+        job_timeout_seconds: Optional[int] = 3600,
+        job_polling_interval_seconds: int = 10,
+    ):
+        """
+        Manages the lifecycle of a Ray cluster and a job by creating a RayJob custom resource.
+        KubeRay operator will then create/delete the RayCluster based on the RayJob definition.
+
+        This method will:
+        1. Generate a RayCluster specification from the provided 'cluster_config'.
+        2. Construct a RayJob custom resource definition using 'job_config' and embedding the RayCluster spec.
+        3. Create the RayJob resource in Kubernetes.
+        4. Optionally, wait for the RayJob to complete or timeout, monitoring its status.
+        5. The RayCluster lifecycle (creation and deletion) is managed by KubeRay
+           based on the RayJob's 'shutdownAfterJobFinishes' field.
+
+        Args:
+            cluster_config: Configuration for the Ray cluster to be created by RayJob.
+            job_config: RayJobSpec object containing job-specific details like entrypoint, runtime_env, etc.
+            job_cr_name: Name for the RayJob Custom Resource. If None, a unique name is generated.
+            submission_mode: How the job is submitted ("K8sJobMode" or "RayClientMode").
+            shutdown_after_job_finishes: If True, RayCluster is deleted after job finishes.
+            ttl_seconds_after_finished: TTL for RayJob after it's finished.
+            suspend_rayjob_creation: If True, creates the RayJob in a suspended state.
+            wait_for_completion: If True, waits for the job to finish.
+            job_timeout_seconds: Timeout for waiting for job completion.
+            job_polling_interval_seconds: Interval for polling job status.
+
+        Returns:
+            A dictionary containing details like RayJob CR name, reported job submission ID,
+            final job status, dashboard URL, and the RayCluster name.
+
+        Raises:
+            TimeoutError: If the job doesn't complete within the specified timeout.
+            ApiException: For Kubernetes API errors.
+            ValueError: For configuration issues.
+        """
+        config_check()
+        k8s_co_api = k8s_client.CustomObjectsApi(get_api_client())
+        namespace = cluster_config.namespace
+
+        if not job_config.entrypoint:
+            raise ValueError("job_config.entrypoint must be specified.")
+
+        # Warn if Pydantic V1/V2 specific fields in RayJobSpec are set, as they are not used for RayJob CR.
+        if (
+            job_config.entrypoint_num_cpus is not None
+            or job_config.entrypoint_num_gpus is not None
+            or job_config.entrypoint_memory is not None
+        ):
+            warnings.warn(
+                "RayJobSpec fields 'entrypoint_num_cpus', 'entrypoint_num_gpus', 'entrypoint_memory' "
+                "are not directly used when creating a RayJob CR. They are primarily for the Ray Job Submission Client. "
+                "Resource requests for the job driver pod should be configured in the RayCluster head node spec via ClusterConfiguration.",
+                UserWarning,
+            )
+
+        # Generate rayClusterSpec from ClusterConfiguration
+        temp_config_for_spec = copy.deepcopy(cluster_config)
+        temp_config_for_spec.appwrapper = False
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", UserWarning)
+            dummy_cluster_for_spec = Cluster(temp_config_for_spec)
+
+        ray_cluster_cr_dict = dummy_cluster_for_spec.resource_yaml
+        if (
+            not isinstance(ray_cluster_cr_dict, dict)
+            or "spec" not in ray_cluster_cr_dict
+        ):
+            raise ValueError(
+                "Failed to generate RayCluster CR dictionary from ClusterConfiguration. "
+                f"Got: {type(ray_cluster_cr_dict)}"
+            )
+        ray_cluster_spec = ray_cluster_cr_dict["spec"]
+
+        # Prepare RayJob CR
+        actual_job_cr_name = job_cr_name or f"rayjob-{uuid.uuid4().hex[:10]}"
+
+        runtime_env_yaml_str = ""
+        if job_config.runtime_env:
+            try:
+                runtime_env_yaml_str = yaml.dump(job_config.runtime_env)
+            except yaml.YAMLError as e:
+                raise ValueError(
+                    f"Invalid job_config.runtime_env, failed to dump to YAML: {e}"
+                )
+
+        ray_job_cr_spec = {
+            "entrypoint": job_config.entrypoint,
+            "shutdownAfterJobFinishes": shutdown_after_job_finishes,
+            "rayClusterSpec": ray_cluster_spec,
+            "submissionMode": submission_mode,
+        }
+
+        if runtime_env_yaml_str:
+            ray_job_cr_spec["runtimeEnvYAML"] = runtime_env_yaml_str
+        if job_config.submission_id:
+            ray_job_cr_spec["jobId"] = job_config.submission_id
+        if job_config.metadata:
+            ray_job_cr_spec["metadata"] = job_config.metadata
+        if ttl_seconds_after_finished is not None:
+            ray_job_cr_spec["ttlSecondsAfterFinished"] = ttl_seconds_after_finished
+        if suspend_rayjob_creation:
+            ray_job_cr_spec["suspend"] = True
+        if job_config.entrypoint_resources:
+            ray_job_cr_spec["entrypointResources"] = job_config.entrypoint_resources
+
+        ray_job_cr = {
+            "apiVersion": "ray.io/v1",
+            "kind": "RayJob",
+            "metadata": {
+                "name": actual_job_cr_name,
+                "namespace": namespace,
+            },
+            "spec": ray_job_cr_spec,
+        }
+
+        returned_job_submission_id = None
+        final_job_status = "UNKNOWN"
+        dashboard_url = None
+        ray_cluster_name_actual = None
+
+        try:
+            print(
+                f"Submitting RayJob '{actual_job_cr_name}' to namespace '{namespace}'..."
+            )
+            k8s_co_api.create_namespaced_custom_object(
+                group="ray.io",
+                version="v1",
+                namespace=namespace,
+                plural="rayjobs",
+                body=ray_job_cr,
+            )
+            print(f"RayJob '{actual_job_cr_name}' created successfully.")
+
+            if wait_for_completion:
+                print(f"Waiting for RayJob '{actual_job_cr_name}' to complete...")
+                start_time = time.time()
+                while True:
+                    try:
+                        ray_job_status_cr = (
+                            k8s_co_api.get_namespaced_custom_object_status(
+                                group="ray.io",
+                                version="v1",
+                                namespace=namespace,
+                                plural="rayjobs",
+                                name=actual_job_cr_name,
+                            )
+                        )
+                    except ApiException as e:
+                        if e.status == 404:
+                            print(
+                                f"RayJob '{actual_job_cr_name}' status not found yet, retrying..."
+                            )
+                            time.sleep(job_polling_interval_seconds)
+                            continue
+                        raise
+
+                    status_field = ray_job_status_cr.get("status", {})
+                    job_deployment_status = status_field.get(
+                        "jobDeploymentStatus", "UNKNOWN"
+                    )
+                    current_job_status = status_field.get("jobStatus", "PENDING")
+
+                    dashboard_url = status_field.get("dashboardURL", dashboard_url)
+                    ray_cluster_name_actual = status_field.get(
+                        "rayClusterName", ray_cluster_name_actual
+                    )
+                    returned_job_submission_id = status_field.get(
+                        "jobId", job_config.submission_id
+                    )
+
+                    final_job_status = current_job_status
+                    print(
+                        f"RayJob '{actual_job_cr_name}' status: JobDeployment='{job_deployment_status}', Job='{current_job_status}'"
+                    )
+
+                    if current_job_status in ["SUCCEEDED", "FAILED", "STOPPED"]:
+                        break
+
+                    if (
+                        job_timeout_seconds
+                        and (time.time() - start_time) > job_timeout_seconds
+                    ):
+                        try:
+                            ray_job_status_cr_final = (
+                                k8s_co_api.get_namespaced_custom_object_status(
+                                    group="ray.io",
+                                    version="v1",
+                                    namespace=namespace,
+                                    plural="rayjobs",
+                                    name=actual_job_cr_name,
+                                )
+                            )
+                            status_field_final = ray_job_status_cr_final.get(
+                                "status", {}
+                            )
+                            final_job_status = status_field_final.get(
+                                "jobStatus", final_job_status
+                            )
+                            returned_job_submission_id = status_field_final.get(
+                                "jobId", returned_job_submission_id
+                            )
+                            dashboard_url = status_field_final.get(
+                                "dashboardURL", dashboard_url
+                            )
+                            ray_cluster_name_actual = status_field_final.get(
+                                "rayClusterName", ray_cluster_name_actual
+                            )
+                        except Exception:
+                            pass
+                        raise TimeoutError(
+                            f"RayJob '{actual_job_cr_name}' timed out after {job_timeout_seconds} seconds. Last status: {final_job_status}"
+                        )
+
+                    time.sleep(job_polling_interval_seconds)
+
+                print(
+                    f"RayJob '{actual_job_cr_name}' finished with status: {final_job_status}"
+                )
+            else:
+                try:
+                    ray_job_status_cr = k8s_co_api.get_namespaced_custom_object_status(
+                        group="ray.io",
+                        version="v1",
+                        namespace=namespace,
+                        plural="rayjobs",
+                        name=actual_job_cr_name,
+                    )
+                    status_field = ray_job_status_cr.get("status", {})
+                    final_job_status = status_field.get("jobStatus", "SUBMITTED")
+                    returned_job_submission_id = status_field.get(
+                        "jobId", job_config.submission_id
+                    )
+                    dashboard_url = status_field.get("dashboardURL", dashboard_url)
+                    ray_cluster_name_actual = status_field.get(
+                        "rayClusterName", ray_cluster_name_actual
+                    )
+                except ApiException as e:
+                    if e.status == 404:
+                        final_job_status = "SUBMITTED_NOT_FOUND"
+                    else:
+                        print(
+                            f"Warning: Could not fetch initial status for RayJob '{actual_job_cr_name}': {e}"
+                        )
+                        final_job_status = "UNKNOWN_API_ERROR"
+
+            return {
+                "job_cr_name": actual_job_cr_name,
+                "job_submission_id": returned_job_submission_id,
+                "job_status": final_job_status,
+                "dashboard_url": dashboard_url,
+                "ray_cluster_name": ray_cluster_name_actual,
+            }
+
+        except ApiException as e:
+            print(
+                f"Kubernetes API error during RayJob '{actual_job_cr_name}' management: {e.reason} (status: {e.status})"
+            )
+            final_status_on_error = "ERROR_BEFORE_SUBMISSION"
+            if actual_job_cr_name:
+                try:
+                    ray_job_status_cr = k8s_co_api.get_namespaced_custom_object_status(
+                        group="ray.io",
+                        version="v1",
+                        namespace=namespace,
+                        plural="rayjobs",
+                        name=actual_job_cr_name,
+                    )
+                    status_field = ray_job_status_cr.get("status", {})
+                    final_status_on_error = status_field.get(
+                        "jobStatus", "UNKNOWN_AFTER_K8S_ERROR"
+                    )
+                except Exception:
+                    final_status_on_error = "UNKNOWN_FINAL_STATUS_FETCH_FAILED"
+            raise
+        except Exception as e:
+            print(
+                f"An unexpected error occurred during managed RayJob execution for '{actual_job_cr_name}': {e}"
+            )
+            raise
+
 
 def list_all_clusters(namespace: str, print_to_console: bool = True):
     """
@@ -760,6 +1057,13 @@ def get_cluster(
         head_extended_resource_requests=head_extended_resources,
         worker_extended_resource_requests=worker_extended_resources,
     )
+    # 1. Prepare RayClusterSpec from ClusterConfiguration
+    # Create a temporary config with appwrapper=False to ensure build_ray_cluster returns RayCluster YAML
+    temp_cluster_config_dict = cluster_config.dict(
+        exclude_none=True
+    )  # Assuming Pydantic V1 or similar .dict() method
+    temp_cluster_config_dict["appwrapper"] = False
+    temp_cluster_config_for_spec = ClusterConfiguration(**temp_cluster_config_dict)
     # Ignore the warning here for the lack of a ClusterConfiguration
     with warnings.catch_warnings():
         warnings.filterwarnings(
@@ -767,7 +1071,7 @@ def get_cluster(
             message="Please provide a ClusterConfiguration to initialise the Cluster object",
         )
         cluster = Cluster(None)
-        cluster.config = cluster_config
+        cluster.config = temp_cluster_config_for_spec
 
         # Remove auto-generated fields like creationTimestamp, uid and etc.
         remove_autogenerated_fields(resource)
diff --git a/src/codeflare_sdk/ray/job/job.py b/src/codeflare_sdk/ray/job/job.py
new file mode 100644
index 0000000..8a6962c
--- /dev/null
+++ b/src/codeflare_sdk/ray/job/job.py
@@ -0,0 +1,173 @@
+from dataclasses import dataclass, field
+from typing import Dict, Any, Optional, Union, List
+from enum import Enum
+
+
+class RayJobStatus(str, Enum):
+    """Status of a RayJob"""
+
+    PENDING = "PENDING"
+    RUNNING = "RUNNING"
+    STOPPED = "STOPPED"
+    SUCCEEDED = "SUCCEEDED"
+    FAILED = "FAILED"
+
+
+@dataclass
+class RayJobSpec:
+    """Specification for a RayJob Custom Resource"""
+
+    # Required fields
+    entrypoint: str
+    """The command to execute for this job (e.g., "python script.py")"""
+
+    # Optional fields
+    submission_id: Optional[str] = None
+    """Unique ID for the job submission. If not provided, one will be generated."""
+
+    runtime_env: Optional[Dict[str, Any]] = None
+    """Runtime environment configuration for the job, including:
+    - working_dir: Directory containing files that your job will run in
+    - pip: List of pip packages to install
+    - conda: Conda environment specification
+    - env_vars: Environment variables to set
+    - py_modules: Python modules to include
+    """
+
+    metadata: Optional[Dict[str, str]] = None
+    """Arbitrary metadata to store with the job"""
+
+    entrypoint_num_cpus: Optional[Union[int, float]] = None
+    """Number of CPU cores to reserve for the entrypoint command"""
+
+    entrypoint_num_gpus: Optional[Union[int, float]] = None
+    """Number of GPUs to reserve for the entrypoint command"""
+
+    entrypoint_memory: Optional[int] = None
+    """Amount of memory to reserve for the entrypoint command"""
+
+    entrypoint_resources: Optional[Dict[str, float]] = None
+    """Custom resources to reserve for the entrypoint command"""
+
+    cluster_name: Optional[str] = None
+    """Name of the RayCluster to run this job on"""
+
+    cluster_namespace: Optional[str] = None
+    """Namespace of the RayCluster to run this job on"""
+
+    # Status fields (managed by the controller)
+    status: RayJobStatus = field(default=RayJobStatus.PENDING)
+    """Current status of the job"""
+
+    message: Optional[str] = None
+    """Detailed status message"""
+
+    start_time: Optional[str] = None
+    """Time when the job started"""
+
+    end_time: Optional[str] = None
+    """Time when the job ended"""
+
+    driver_info: Optional[Dict[str, str]] = None
+    """Information about the job driver, including:
+    - id: Driver ID
+    - node_ip_address: IP address of the node running the driver
+    - pid: Process ID of the driver
+    """
+
+
+@dataclass
+class RayJob:
+    """RayJob Custom Resource Definition"""
+
+    metadata: Dict[str, Any]
+    """Kubernetes metadata for the job"""
+
+    spec: RayJobSpec
+    """Job specification"""
+
+    api_version: str = "ray.io/v1"
+    kind: str = "RayJob"
+
+    status: Optional[Dict[str, Any]] = None
+    """Status of the job (managed by the controller)"""
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert the RayJob to a dictionary suitable for Kubernetes API"""
+        return {
+            "apiVersion": self.api_version,
+            "kind": self.kind,
+            "metadata": self.metadata,
+            "spec": {
+                "entrypoint": self.spec.entrypoint,
+                "submission_id": self.spec.submission_id,
+                "runtime_env": self.spec.runtime_env,
+                "metadata": self.spec.metadata,
+                "entrypoint_num_cpus": self.spec.entrypoint_num_cpus,
+                "entrypoint_num_gpus": self.spec.entrypoint_num_gpus,
+                "entrypoint_memory": self.spec.entrypoint_memory,
+                "entrypoint_resources": self.spec.entrypoint_resources,
+                "cluster_name": self.spec.cluster_name,
+                "cluster_namespace": self.spec.cluster_namespace,
+            },
+            "status": {
+                "status": self.spec.status,
+                "message": self.spec.message,
+                "start_time": self.spec.start_time,
+                "end_time": self.spec.end_time,
+                "driver_info": self.spec.driver_info,
+            }
+            if self.status is None
+            else self.status,
+        }
+
+    def apply(self, force=False):
+        """
+        Applies the RayJob using server-side apply.
+        If 'force' is set to True, conflicts will be forced.
+
+        Args:
+            force (bool): If True, force conflicts during server-side apply.
+        """
+        from kubernetes import client
+        from kubernetes.dynamic import DynamicClient
+        from ...common.kubernetes_cluster.auth import get_api_client, config_check
+        from ...common import _kube_api_error_handling
+
+        CF_SDK_FIELD_MANAGER = "codeflare-sdk"
+
+        try:
+            # Check Kubernetes configuration
+            config_check()
+
+            # Get the dynamic client
+            crds = DynamicClient(get_api_client()).resources
+
+            # Get the RayJob API instance
+            api_version = "ray.io/v1"
+            api_instance = crds.get(api_version=api_version, kind="RayJob")
+
+            # Get namespace from metadata
+            namespace = self.metadata.get("namespace", "default")
+            name = self.metadata.get("name")
+
+            # Convert job to dictionary
+            body = self.to_dict()
+
+            # Apply the job using server-side apply
+            api_instance.server_side_apply(
+                field_manager=CF_SDK_FIELD_MANAGER,
+                group="ray.io",
+                version="v1",
+                namespace=namespace,
+                plural="rayjobs",
+                body=body,
+                force_conflicts=force,
+            )
+
+            print(f"RayJob: '{name}' has successfully been applied")
+
+        except AttributeError as e:
+            raise RuntimeError(f"Failed to initialize DynamicClient: {e}")
+        except Exception as e:
+            return _kube_api_error_handling(e)
diff --git a/src/codeflare_sdk/ray/rayjobs/__init__.py b/src/codeflare_sdk/ray/rayjobs/__init__.py
new file mode 100644
index 0000000..d9cbae3
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/__init__.py
@@ -0,0 +1 @@
+from .rayjob import RayJob
diff --git a/src/codeflare_sdk/ray/rayjobs/rayjob.py b/src/codeflare_sdk/ray/rayjobs/rayjob.py
new file mode 100644
index 0000000..e7a9a58
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/rayjob.py
@@ -0,0 +1,111 @@
+"""
+RayJob client for submitting and managing Ray jobs using the odh-kuberay-client.
+"""
+
+import logging
+from typing import Dict, Any, Optional
+from odh_kuberay_client.kuberay_job_api import RayjobApi
+
+# Set up logging
+logger = logging.getLogger(__name__)
+
+
+class RayJob:
+    """
+    A client for managing Ray jobs using the KubeRay operator.
+
+    This class provides a simplified interface for submitting and managing
+    Ray jobs in a Kubernetes cluster with the KubeRay operator installed.
+    """
+
+    def __init__(
+        self,
+        job_name: str,
+        cluster_name: str,
+        namespace: str = "default",
+        entrypoint: str = "None",
+        runtime_env: Optional[Dict[str, Any]] = None,
+    ):
+        """
+        Initialize a RayJob instance.
+
+        Args:
+            name: The name for the Ray job
+            namespace: The Kubernetes namespace to submit the job to (default: "default")
+            cluster_name: The name of the Ray cluster to submit the job to
+            **kwargs: Additional configuration options
+        """
+        self.name = job_name
+        self.namespace = namespace
+        self.cluster_name = cluster_name
+        self.entrypoint = entrypoint
+        self.runtime_env = runtime_env
+
+        # Initialize the KubeRay job API client
+        self._api = RayjobApi()
+
+        logger.info(f"Initialized RayJob: {self.name} in namespace: {self.namespace}")
+
+    def submit(
+        self,
+    ) -> str:
+        """
+        Submit the Ray job to the Kubernetes cluster.
+
+        Args:
+            entrypoint: The Python script or command to run
+            runtime_env: Ray runtime environment configuration (optional)
+
+        Returns:
+            The job ID/name if submission was successful
+
+        Raises:
+            RuntimeError: If the job has already been submitted or submission fails
+        """
+        # Build the RayJob custom resource
+        rayjob_cr = self._build_rayjob_cr(
+            entrypoint=self.entrypoint,
+            runtime_env=self.runtime_env,
+        )
+
+        # Submit the job
+        logger.info(
+            f"Submitting RayJob {self.name} to RayCluster {self.cluster_name} in namespace {self.namespace}"
+        )
+        result = self._api.submit_job(k8s_namespace=self.namespace, job=rayjob_cr)
+
+        if result:
+            logger.info(f"Successfully submitted RayJob {self.name}")
+            return self.name
+        else:
+            raise RuntimeError(f"Failed to submit RayJob {self.name}")
+
+    def _build_rayjob_cr(
+        self,
+        entrypoint: str,
+        runtime_env: Optional[Dict[str, Any]] = None,
+    ) -> Dict[str, Any]:
+        """
+        Build the RayJob custom resource specification.
+
+        This creates a minimal RayJob CR that can be extended later.
+        """
+        # Basic RayJob custom resource structure
+        rayjob_cr = {
+            "apiVersion": "ray.io/v1",
+            "kind": "RayJob",
+            "metadata": {
+                "name": self.name,
+                "namespace": self.namespace,
+            },
+            "spec": {
+                "entrypoint": entrypoint,
+                "clusterSelector": {"ray.io/cluster": self.cluster_name},
+            },
+        }
+
+        # Add runtime environment if specified
+        if runtime_env:
+            rayjob_cr["spec"]["runtimeEnvYAML"] = str(runtime_env)
+
+        return rayjob_cr
diff --git a/src/codeflare_sdk/ray/rayjobs/test_rayjob.py b/src/codeflare_sdk/ray/rayjobs/test_rayjob.py
new file mode 100644
index 0000000..dccab7d
--- /dev/null
+++ b/src/codeflare_sdk/ray/rayjobs/test_rayjob.py
@@ -0,0 +1,60 @@
+# Copyright 2024 IBM, Red Hat
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from unittest.mock import MagicMock
+from codeflare_sdk.ray.rayjobs.rayjob import RayJob
+
+
+def test_rayjob_submit_success(mocker):
+    """Test successful RayJob submission."""
+    # Mock kubernetes config loading
+    mocker.patch("kubernetes.config.load_kube_config")
+
+    # Mock the RayjobApi class entirely
+    mock_api_class = mocker.patch("codeflare_sdk.ray.rayjobs.rayjob.RayjobApi")
+    mock_api_instance = MagicMock()
+    mock_api_class.return_value = mock_api_instance
+
+    # Configure the mock to return success when submit is called
+    mock_api_instance.submit.return_value = {"metadata": {"name": "test-rayjob"}}
+
+    # Create RayJob instance
+    rayjob = RayJob(
+        job_name="test-rayjob",
+        cluster_name="test-ray-cluster",
+        namespace="test-namespace",
+        entrypoint="python -c 'print(\"hello world\")'",
+        runtime_env={"pip": ["requests"]},
+    )
+
+    # Submit the job
+    job_id = rayjob.submit()
+
+    # Assertions
+    assert job_id == "test-rayjob"
+
+    # Verify the API was called with correct parameters
+    mock_api_instance.submit.assert_called_once()
+    call_args = mock_api_instance.submit.call_args
+
+    # Check the namespace parameter
+    assert call_args.kwargs["k8s_namespace"] == "test-namespace"
+
+    # Check the job custom resource
+    job_cr = call_args.kwargs["job"]
+    assert job_cr["metadata"]["name"] == "test-rayjob"
+    assert job_cr["metadata"]["namespace"] == "test-namespace"
+    assert job_cr["spec"]["entrypoint"] == "python -c 'print(\"hello world\")'"
+    assert job_cr["spec"]["clusterSelector"]["ray.io/cluster"] == "test-ray-cluster"
+    assert job_cr["spec"]["runtimeEnvYAML"] == "{'pip': ['requests']}"
diff --git a/test.ipynb b/test.ipynb
new file mode 100644
index 0000000..84861a6
--- /dev/null
+++ b/test.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8f8ed15a",
+   "metadata": {},
+   "source": [
+    "## Steps to reproduce the Job Agent bug\n",
+    "To get the below wheel file for codeflare-sdk set up you can run the following in your terminal \n",
+    "```bash\n",
+    "poetry lock\n",
+    "poetry install\n",
+    "poetry build\n",
+    "```\n",
+    "\n",
+    "Then you can run the below to install it. This is using a dev image of the kuberay python client found here -> https://test.pypi.org/project/odh-kuberay-client/#history. The referenced kuberay python client version is working as intended. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10c8c12e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ dist/codeflare_sdk-0.0.0.dev0-py3-none-any.whl --force-reinstall"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e021c61",
+   "metadata": {},
+   "source": [
+    "Below is the code I used to confirm that Kuberay Python Client is functional. It uses the cluster api to create a cluster, and submitting a job to this is successful. You don't need to run this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5dc1a0fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from odh_kuberay_client import kuberay_cluster_api\n",
+    "from odh_kuberay_client.utils import kuberay_cluster_builder\n",
+    "\n",
+    "cluster_name = \"working\"\n",
+    "namespace = \"default\"\n",
+    "\n",
+    "director = kuberay_cluster_builder.Director()\n",
+    "cluster_api = kuberay_cluster_api.RayClusterApi()\n",
+    "\n",
+    "# Build a small cluster\n",
+    "cluster_body = director.build_small_cluster(\n",
+    "    name=cluster_name,\n",
+    "    k8s_namespace=namespace,\n",
+    "    labels={\"ray.io/cluster\": cluster_name},\n",
+    ")\n",
+    "\n",
+    "created_cluster = cluster_api.create_ray_cluster(\n",
+    "    body=cluster_body, k8s_namespace=namespace\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "005c2ba2",
+   "metadata": {},
+   "source": [
+    "The below uses the default ray image. Creating this cluster and submitting a job will work. To recreate the bug, comment out the `image=\"ray-project/ray:2.47.1\"` line and submit the job again. Ensure the RayJob CR from the working job submission was deleted.  \n",
+    "\n",
+    "You will need a Kind cluster with Kuberay installed for this to work FYI!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb80c155",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from codeflare_sdk import Cluster, ClusterConfiguration\n",
+    "\n",
+    "cluster = Cluster(ClusterConfiguration(\n",
+    "    name='test-cluster',\n",
+    "    namespace='default',\n",
+    "    head_cpu_requests='1',\n",
+    "    head_cpu_limits='2',\n",
+    "    head_memory_requests=4,\n",
+    "    head_memory_limits=5,\n",
+    "    head_extended_resource_requests={'nvidia.com/gpu':0},\n",
+    "    worker_extended_resource_requests={'nvidia.com/gpu':0},\n",
+    "    num_workers=1,\n",
+    "    worker_cpu_requests='1',\n",
+    "    worker_cpu_limits='2',\n",
+    "    worker_memory_requests=3,\n",
+    "    worker_memory_limits=4,\n",
+    "    image=\"ray-project/ray:2.47.1\" # this image works, omit this line to use our default image which does not work\n",
+    "))\n",
+    "\n",
+    "cluster.apply()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94841b95",
+   "metadata": {},
+   "source": [
+    "With the below, a pod gets created which submits the job. This will be marked as completed if its successful and you can view the job in the Dashboard as normal. If not, it will fail, and the CR will try to recreate new pods in a loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5e3c895",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from odh_kuberay_client.kuberay_job_api import RayjobApi\n",
+    "\n",
+    "rayjob_api = RayjobApi()\n",
+    "\n",
+    "job_body = {\n",
+    "    \"apiVersion\": \"ray.io/v1\",\n",
+    "    \"kind\": \"RayJob\",\n",
+    "    \"metadata\": {\n",
+    "        \"name\": \"test-job\",\n",
+    "        \"namespace\": \"default\",\n",
+    "        \"labels\": {\n",
+    "            \"app.kubernetes.io/name\": \"test-job\",\n",
+    "            \"app.kubernetes.io/managed-by\": \"kuberay\",\n",
+    "        },\n",
+    "    },\n",
+    "    \"spec\": {\n",
+    "        \"clusterSelector\": {\n",
+    "            \"ray.io/cluster\": \"test-cluster\",\n",
+    "        },\n",
+    "        \"entrypoint\": 'python -c \"import time; time.sleep(20)\"',\n",
+    "        \"submissionMode\": \"K8sJobMode\",\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "rayjob_api.submit_job(job=job_body, k8s_namespace=\"default\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}