Do I need to engineer lagged features when creating an LSTM for time series forecasting?
TL;DR

No, you don't have to include lagged variables when using an LSTM.

Long Answer

In an LSTM architecture, your neurons have not only an input gate and an output gate, but they also have a forget gate which causes them to remember values over multiple time intervals. So, you don't have to include any lagged variables in your feature set, since you can expect the LSTM to discover relevant time-depended relationships on its own.
This is not only the case for LSTM's but also for other RNN-architectures.