That $T$ is linear is obvious: $T(cx + y) = \alpha(t)\bigl(cx(t) + y(t)\bigr) = c\alpha(t)x(t) + \alpha(t)y(t) = cTx + Ty$. Now, if $\alpha$ is essentially bounded, then there exists $M > 0$ such that the measure of the set of all $t$ where $|\alpha(t)|> M$ is zero. Therefore, outside a set of zero measure, $|Tx| = |\alpha(t)x(t)| = |\alpha(t)||x(t)|\leq M|x(t)|$. Therefore $Tx\in L^2(E)$. Finally, to prove that $T$ is bounded, by the above inequality, we have
$\int_E|Tx|^2d\mu \leq M^2\int_E|x|^2 d\mu $
In other words, $\|Tx\|_{L^2}\leq M\|x\|_{L^2}$. Hence, by definition, the operator $T$ is bounded, and $\|T\|\leq M$.
EDIT: The other direction.
Assume now that $T$, as defined, is a bounded linear operator on $L^2(E)$. Assuming that $\alpha$ is not essentially bounded on $E$, we get that for $\epsilon > 0$ there exists a positive measure set $\widetilde{E}\subset E$ such that $|\alpha(t)| > (\|T\| + \epsilon)^{1/2}$ for all $t\in \widetilde{E}$. Moreover, since $E$ is bounded, so is $\widetilde{E}$, so $0 < \mu(\widetilde{E}) < \infty$. Now take $x(t) = \mu(\widetilde{E})^{-1/2}\chi_{\widetilde{E}}\in L^2(E)$. We get:
$\int_E |Tx|^2d\mu = \int_E|\alpha|^2|x|^2d\mu = \frac{1}{\mu(\widetilde{E})}\int_{\widetilde{E}}|\alpha|^2d\mu > \|T\| = \|T\|\|x\|_{L^2}.$
This contradicts the fact that $\|T\|$ is the norm of $T$.