If you want to find the proximal operator of $\|x\|_{\infty}$, you don't want to compute the subgradient directly. Rather, as the previous answer mentioned, we can use Moreau decomposition:
$$ v = \textrm{prox}_{f}(v) + \textrm{prox}_{f^*}(v)$$
where $f^*$ is the convex conjugate, given by:
$$ f^*(x) = \underset{y}{\sup}\;(x^Ty - f(y))$$
In the case of norms, the convex conjugate is an indicator function based on the dual norm, i.e. if $f(x) = \|x\|_p$, for $p \geq 1$, then $f^*(x) = 1_{\{\|x\|_q \leq 1\}}(x)$, where $1/p + 1/q = 1$, and the indicator function is:
\begin{equation}
1_S(x)=\begin{cases}
0, & \text{if $x \in S$}.\\
\infty, & \text{if $x \notin S$}.
\end{cases}
\end{equation}
For your particular question, $f(x) = \|x\|_{\infty}$, so $f^*(x) = 1_{\{\|x\|_1\leq 1\}}(x)$.
We know
$$\textrm{prox}_{f}(x) = x - \textrm{prox}_{f^*}(x)$$
Thus we need to find
$$\textrm{prox}_{f^*}(x) = \underset{z}{\arg\min} \; \left(1_{\{\|z\|_1 \leq 1\}} + \|z - x\|_2^2 \right)$$
But this is simply projection onto the $L_1$ ball, thus the prox of the infinity norm is given by:
$$ \textrm{prox}_{\|\cdot\|_{\infty}}(x) = x - \textrm{Proj}_{\{\|\cdot\|_1 \leq 1\}}(x)$$
The best reference for this is Neal Parikh, Stephen Boyd - Proximal Algorithms.