The following ended up being the solution:
resource "aws_sagemaker_model" "huggingface" {
  name               = "bertModel"
  execution_role_arn = "arn:aws:iam::***************:role/apps/my-sagemaker-role"


  primary_container {
    # CPU Image
    image="763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-inference:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
    model_data_url="s3://my-s3-bucket/model.tar.gz"

    environment = {
      HF_TASK          = "feature-extraction"
    }
  }
}

resource "aws_sagemaker_endpoint_configuration" "huggingface" {
  name = "bertEndpointConfig"

  production_variants {
    variant_name           = "variant-1"
    model_name             = aws_sagemaker_model.huggingface.name
    initial_instance_count = 1
    instance_type          = "ml.t2.medium"
  }
}

resource "aws_sagemaker_endpoint" "huggingface" {
  name                 = "tfDeployedModel"
  endpoint_config_name = aws_sagemaker_endpoint_configuration.huggingface.name
}

The big issue came from a newer image uri which was causing an exception in my custom post processing code.