  The upcoming ByCycle project on the VISTA/4MOST multi-object spectrograph
will offer new prospects of using a massive sample of $\sim 1$ million high
spectral resolution ($R$ = 20,000) background quasars to map the circumgalactic
metal content of foreground galaxies (observed at $R$ = 4000 - 7000), as traced
by metal absorption. Such large surveys require specialized analysis
methodologies. In the absence of early data, we instead produce synthetic 4MOST
high-resolution fibre quasar spectra. To do so, we use the TNG50 cosmological
magnetohydrodynamical simulation, combining photo-ionization post-processing
and ray tracing, to capture MgII ($\lambda2796$, $\lambda2803$) absorbers. We
then use this sample to train a Convolutional Neural Network (CNN) which
searches for, and estimates the redshift of, MgII absorbers within these
spectra. For a test sample of quasar spectra with uniformly distributed
properties ($\lambda_{\rm{MgII,2796}}$, $\rm{EW}_{\rm{MgII,2796}}^{\rm{rest}} =
0.05 - 5.15$ \AA, $\rm{SNR} = 3 - 50$), the algorithm has a robust
classification accuracy of 98.6 per cent and a mean wavelength accuracy of 6.9
\AA. For high signal-to-noise spectra ($\rm{SNR > 20}$), the algorithm robustly
detects and localizes MgII absorbers down to equivalent widths of
$\rm{EW}_{\rm{MgII,2796}}^{\rm{rest}} = 0.05$ \AA. For the lowest SNR spectra
($\rm{SNR=3}$), the CNN reliably recovers and localizes
EW$_{\rm{MgII,2796}}^{\rm{rest}}$ $\geq$ 0.75 \AA\, absorbers. This is more
than sufficient for subsequent Voigt profile fitting to characterize the
detected MgII absorbers. We make the code publicly available through GitHub.
Our work provides a proof-of-concept for future analyses of quasar spectra
datasets numbering in the millions, soon to be delivered by the next generation
of surveys.
