diff --git a/compiler/lib/Conversion/FHETensorOpsToLinalg/TensorOpsToLinalg.cpp b/compiler/lib/Conversion/FHETensorOpsToLinalg/TensorOpsToLinalg.cpp
index 85b2153a5..97df64aa7 100644
--- a/compiler/lib/Conversion/FHETensorOpsToLinalg/TensorOpsToLinalg.cpp
+++ b/compiler/lib/Conversion/FHETensorOpsToLinalg/TensorOpsToLinalg.cpp
@@ -1243,6 +1243,86 @@ struct SumToLinalgGeneric
   };
 };
 
+// This rewrite pattern transforms any instance of operators
+// `FHELinalg.transpose` to an instance of `linalg.generic`.
+//
+// Example:
+//
+//   %result = "FHELinalg.transpose"(%input: tensor<d0xd1x...xdNx!FHE.eint<p>>)
+//   -> tensor<dNx...xd1xd0x!FHE.eint<p>
+//
+// becomes:
+//
+//   #map0 = affine_map<(i0, i1, ..., iN) -> (iN, ..., i1, i0)>
+//   #map1 = affine_map<(i0, i1, ..., iN) -> (i0, i1, ..., iN)>
+//
+//   %accumulator = "FHE.zero_tensor"() : () ->
+//   tensor<dNx...xd1xd0x!FHE.eint<6>> %result = linalg.generic
+//     {
+//       indexing_maps = [#map0, #map1],
+//       iterator_types = ["parallel", "parallel", ..., "parallel"]
+//     }
+//     ins(%input : tensor<d0xd1x...xdNx!FHE.eint<7>>)
+//     outs(%accumulator : tensor<dNx...xd1xd0x!FHE.eint<7>>)
+//     {
+//       ^bb0(%a: !FHE.eint<7>, %b: !FHE.eint<7>):
+//         linalg.yield %a : !FHE.eint<7>
+//     } -> tensor<dNx...xd1xd0x!FHE.eint<7>>
+//
+struct TransposeToLinalgGeneric
+    : public ::mlir::OpRewritePattern<
+          mlir::concretelang::FHELinalg::TransposeOp> {
+  TransposeToLinalgGeneric(::mlir::MLIRContext *context)
+      : ::mlir::OpRewritePattern<::mlir::concretelang::FHELinalg::TransposeOp>(
+            context, mlir::concretelang::DEFAULT_PATTERN_BENEFIT) {}
+
+  ::mlir::LogicalResult
+  matchAndRewrite(::mlir::concretelang::FHELinalg::TransposeOp transposeOp,
+                  ::mlir::PatternRewriter &rewriter) const override {
+
+    mlir::Value input = transposeOp.getOperand();
+    mlir::Value output = transposeOp.getResult();
+    auto inputType = input.getType().dyn_cast<mlir::RankedTensorType>();
+    auto outputType = output.getType().dyn_cast<mlir::RankedTensorType>();
+
+    mlir::Location location = transposeOp.getLoc();
+    // Initialize empty tensor to fill with transpose result
+    mlir::Value zeroTensor =
+        rewriter.create<FHE::ZeroTensorOp>(location, outputType).getResult();
+
+    // Inverted dimensions to create a transposition
+    std::vector<unsigned int> perms = {};
+    auto n_dim = inputType.getShape().size();
+    for (int i = n_dim - 1; i >= 0; i--)
+      perms.push_back(i);
+
+    llvm::SmallVector<mlir::Type, 1> resultTypes{zeroTensor.getType()};
+    auto ins = llvm::SmallVector<mlir::Value, 1>{input};
+    auto outs = llvm::SmallVector<mlir::Value, 1>{zeroTensor};
+    llvm::SmallVector<mlir::AffineMap, 2> maps{
+        mlir::AffineMap::getPermutationMap(perms, this->getContext()),
+        mlir::AffineMap::getMultiDimIdentityMap(n_dim, this->getContext()),
+    };
+    auto iteratorTypes = parallelIteratorType(n_dim);
+    // The maps will be responsible for changing item positions, we just return
+    // items here
+    auto regionBuilder = [&](mlir::OpBuilder &nestedBuilder,
+                             mlir::Location nestedLoc,
+                             mlir::ValueRange blockArgs) {
+      mlir::Value item = blockArgs[0];
+      nestedBuilder.create<linalg::YieldOp>(location, item);
+    };
+    mlir::Value result =
+        rewriter
+            .create<linalg::GenericOp>(location, resultTypes, ins, outs, maps,
+                                       iteratorTypes, regionBuilder)
+            .getResult(0);
+
+    rewriter.replaceOp(transposeOp, {result});
+    return mlir::success();
+  };
+};
+
 // This rewrite pattern transforms any instance of operators
 // `FHELinalg.concat` to instances of `tensor.insert_slice`
 //
@@ -1558,6 +1638,7 @@ void FHETensorOpsToLinalg::runOnFunction() {
   patterns.insert<SumToLinalgGeneric>(&getContext());
   patterns.insert<ConcatRewritePattern>(&getContext());
   patterns.insert<FHELinalgConv2dToLinalgConv2d>(&getContext());
+  patterns.insert<TransposeToLinalgGeneric>(&getContext());
 
   if (mlir::applyPartialConversion(function, target, std::move(patterns))
           .failed())