  The Laser Interferometer Space Antenna (LISA), due for launch in the mid
2030s, is expected to observe gravitational waves (GW)s from merging massive
black hole binaries (MBHB)s. These signals can last from days to months,
depending on the masses of the black holes, and are expected to be observed
with high signal to noise ratios (SNR)s out to high redshifts. We have adapted
the PyCBC software package to enable a template bank search and inference of
GWs from MBHBs. The pipeline is tested on the LISA data challenge (LDC)'s
Challenge 2a (\enquote{Sangria}), which contains MBHBs and thousands of
galactic binaries (GBs) in simulated instrumental LISA noise. Our search
identifies all 6 MBHB signals with more than $92\%$ of the optimal SNR. The
subsequent parameter inference step recovers the masses and spins within their
$90\%$ confidence interval. Sky position parameters have 8 high likelihood
modes which are recovered but often our posteriors favour the incorrect sky
mode. We observe that the addition of GBs biases the parameter recovery of
masses and spins away from the injected values, reinforcing the need for a
global fit pipeline which will simultaneously fit the parameters of the GB
signals before estimating the parameters of MBHBs.
