diff --git a/nemo_retriever/src/nemo_retriever/ingest_modes/batch.py b/nemo_retriever/src/nemo_retriever/ingest_modes/batch.py
index 0c40a9e10..1d98ffb29 100644
--- a/nemo_retriever/src/nemo_retriever/ingest_modes/batch.py
+++ b/nemo_retriever/src/nemo_retriever/ingest_modes/batch.py
@@ -71,12 +71,7 @@ def _debug_log(*, logger: logging.Logger, location: str, message: str, data: dic
         logger.debug("%s | %s | %r", location, message, data)
 
 
-def _coerce_params[T](params: T | None, model_cls: type[T], kwargs: dict[str, Any]) -> T:
-    if params is None:
-        return model_cls(**kwargs)
-    if kwargs:
-        return params.model_copy(update=kwargs)  # type: ignore[return-value]
-    return params
+from nemo_retriever.params.utils import coerce_params as _coerce_params
 
 
 class _LanceDBWriteActor:
@@ -712,17 +707,10 @@ def embed(
                 "No Ray Dataset to embed. Provide input_dataset or run .files(...) / .extract(...) first."
             )
 
-        resolved = _coerce_params(params, EmbedParams, kwargs)
-        kwargs = {
-            **resolved.model_dump(
-                mode="python", exclude={"runtime", "batch_tuning", "fused_tuning"}, exclude_none=True
-            ),
-            **resolved.runtime.model_dump(mode="python", exclude_none=True),
-            **resolved.batch_tuning.model_dump(mode="python", exclude_none=True),
-        }
+        from nemo_retriever.params.utils import build_embed_kwargs
 
-        if "embedding_endpoint" not in kwargs and kwargs.get("embed_invoke_url"):
-            kwargs["embedding_endpoint"] = kwargs.get("embed_invoke_url")
+        resolved = _coerce_params(params, EmbedParams, kwargs)
+        kwargs = build_embed_kwargs(resolved, include_batch_tuning=True)
 
         # Remaining kwargs are forwarded to the actor constructor.
         embed_modality = resolved.embed_modality
diff --git a/nemo_retriever/src/nemo_retriever/ingest_modes/inprocess.py b/nemo_retriever/src/nemo_retriever/ingest_modes/inprocess.py
index 45d6373e7..dd2854ae7 100644
--- a/nemo_retriever/src/nemo_retriever/ingest_modes/inprocess.py
+++ b/nemo_retriever/src/nemo_retriever/ingest_modes/inprocess.py
@@ -64,12 +64,7 @@
 _CONTENT_COLUMNS = ("table", "chart", "infographic")
 
 
-def _coerce_params[T](params: T | None, model_cls: type[T], kwargs: dict[str, Any]) -> T:
-    if params is None:
-        return model_cls(**kwargs)
-    if kwargs:
-        return params.model_copy(update=kwargs)  # type: ignore[return-value]
-    return params
+from nemo_retriever.params.utils import coerce_params as _coerce_params
 
 
 def _combine_text_with_content(row, text_column, content_columns):
@@ -1286,14 +1281,9 @@ def embed(self, params: EmbedParams | None = None, **kwargs: Any) -> "InProcessI
                 )
             )
 
-        embed_kwargs = {
-            **resolved.model_dump(
-                mode="python", exclude={"runtime", "batch_tuning", "fused_tuning"}, exclude_none=True
-            ),
-            **resolved.runtime.model_dump(mode="python", exclude_none=True),
-        }
-        if "embedding_endpoint" not in embed_kwargs and embed_kwargs.get("embed_invoke_url"):
-            embed_kwargs["embedding_endpoint"] = embed_kwargs.get("embed_invoke_url")
+        from nemo_retriever.params.utils import build_embed_kwargs
+
+        embed_kwargs = build_embed_kwargs(resolved)
 
         # Ensure embed_modality is forwarded to the embedding function.
         embed_kwargs["embed_modality"] = embed_modality
diff --git a/nemo_retriever/src/nemo_retriever/params/utils.py b/nemo_retriever/src/nemo_retriever/params/utils.py
new file mode 100644
index 000000000..8b076e169
--- /dev/null
+++ b/nemo_retriever/src/nemo_retriever/params/utils.py
@@ -0,0 +1,43 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-25, NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Shared parameter coercion and building helpers used by ingest modes."""
+
+from __future__ import annotations
+
+from typing import Any, Dict
+
+
+def coerce_params[T](params: T | None, model_cls: type[T], kwargs: dict[str, Any]) -> T:
+    """Merge *params* and *kwargs* into an instance of *model_cls*.
+
+    - If *params* is ``None``, construct from *kwargs*.
+    - If *kwargs* is non-empty, apply them as overrides via ``model_copy``.
+    - Otherwise return *params* unchanged.
+    """
+    if params is None:
+        return model_cls(**kwargs)
+    if kwargs:
+        return params.model_copy(update=kwargs)  # type: ignore[return-value]
+    return params
+
+
+def build_embed_kwargs(resolved: Any, *, include_batch_tuning: bool = False) -> Dict[str, Any]:
+    """Flatten an ``EmbedParams`` instance into a dict ready for actor/task kwargs.
+
+    Merges ``runtime`` (always) and optionally ``batch_tuning`` sub-models.
+    Also normalises ``embed_invoke_url`` → ``embedding_endpoint``.
+    """
+    exclude = {"runtime", "batch_tuning", "fused_tuning"}
+    kwargs: Dict[str, Any] = {
+        **resolved.model_dump(mode="python", exclude=exclude, exclude_none=True),
+        **resolved.runtime.model_dump(mode="python", exclude_none=True),
+    }
+    if include_batch_tuning:
+        kwargs.update(resolved.batch_tuning.model_dump(mode="python", exclude_none=True))
+
+    if "embedding_endpoint" not in kwargs and kwargs.get("embed_invoke_url"):
+        kwargs["embedding_endpoint"] = kwargs["embed_invoke_url"]
+
+    return kwargs
diff --git a/nemo_retriever/tests/test_params_utils.py b/nemo_retriever/tests/test_params_utils.py
new file mode 100644
index 000000000..28e4c80a5
--- /dev/null
+++ b/nemo_retriever/tests/test_params_utils.py
@@ -0,0 +1,55 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-25, NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Unit tests for nemo_retriever.params.utils."""
+
+from nemo_retriever.params.models import EmbedParams
+from nemo_retriever.params.utils import build_embed_kwargs, coerce_params
+
+
+class TestCoerceParams:
+    def test_none_params_constructs_from_kwargs(self):
+        result = coerce_params(None, EmbedParams, {"embed_modality": "image"})
+        assert isinstance(result, EmbedParams)
+        assert result.embed_modality == "image"
+
+    def test_params_without_kwargs_returned_unchanged(self):
+        original = EmbedParams(embed_modality="text")
+        result = coerce_params(original, EmbedParams, {})
+        assert result is original
+
+    def test_params_with_kwargs_applies_overrides(self):
+        original = EmbedParams(embed_modality="text")
+        result = coerce_params(original, EmbedParams, {"embed_modality": "image"})
+        assert result.embed_modality == "image"
+        assert result is not original
+
+
+class TestBuildEmbedKwargs:
+    def test_normalises_embed_invoke_url(self):
+        params = EmbedParams(embed_invoke_url="http://nim:8000/v1")
+        kwargs = build_embed_kwargs(params)
+        assert kwargs["embedding_endpoint"] == "http://nim:8000/v1"
+
+    def test_does_not_overwrite_existing_embedding_endpoint(self):
+        params = EmbedParams(
+            embed_invoke_url="http://old:8000/v1",
+        )
+        kwargs = build_embed_kwargs(params)
+        assert "embedding_endpoint" in kwargs
+
+    def test_includes_batch_tuning_when_requested(self):
+        params = EmbedParams()
+        with_bt = build_embed_kwargs(params, include_batch_tuning=True)
+        without_bt = build_embed_kwargs(params, include_batch_tuning=False)
+        # batch_tuning keys should be present when included
+        assert isinstance(with_bt, dict)
+        assert isinstance(without_bt, dict)
+
+    def test_excludes_nested_sub_models(self):
+        params = EmbedParams()
+        kwargs = build_embed_kwargs(params)
+        assert "runtime" not in kwargs
+        assert "batch_tuning" not in kwargs
+        assert "fused_tuning" not in kwargs