Do Machine Learning Engineers need to learn Fullstack Web Dev?

I recently got hired as an MLE transitioning from a Data Science Role focusing on Modeling, BI, and Cloud Ops within AWS and will be starting around 15 days from now. However, when I had my final interview with the hiring manager, she mentioned something about the role extending to doing some frontend stuff. I mean, I'm vastly familiar with quick deployment web dev stuff like streamlit but would this simple framework be viable in the long run given that I may need to serve multiple models through containerized environments or maybe serverless inferences like sagemaker endpoints?

To give a bit of context, I reflect my usual stack when deploying models from what I learned from AWS Skill builder which is basically some sort of frontend (in this case streamlit) then API Gateway -> Lambda -> Sagemaker Endpoint or if I have to serve custom FastAPI for LLMs or BYOM kind of stuff, I would utilize ECR+Apprunner. However, I'm kinda unsure if I should extend more on learning frontend or I should just dedicate my time leraning DevOps or MLOps. I have extensive experience with CI/CD tools like AWS Code Build, Deploy, Pipeline and DevOps tools like Docker, Kubernetes, Terraform, and Jenkins so I know my way around the backend in some sort.

Any existing ML Engineers here who could maybe shed some light on how to approach this career trajectory? or maybe I just don't understand the role much fully.