I'm going to assume that you mean that the probability of your distribution being 1 given the values of $\pi_1$ and $\pi_2$ is $\pi_1$. Similar definitions for the distribution being 2 or 3. In addition, I will assume that there are $n_j$ observations of $j$, with $n_1+n_2+n_3=n$. Then we have the following:
\begin{align}
L(\pi_1,\pi_2)&=\pi_1^{n_1}\pi_2^{n_2}(1-\pi_1-\pi_2)^{n_3}\\
l(\pi_1,\pi_2)&=n_1\ln\pi_1+n_2\ln\pi_2+n_3\ln(1-\pi_1-\pi_3)\\
l_1&=\frac{n_1}{\pi_1}-\frac{n_3}{1-\pi_1-\pi_2}=0\\
\frac{1}{1-\pi_1-\pi_2}&=\frac{n_1}{n_3\cdot\pi_1}\\
l_2&=\frac{n_1}{\pi_2}-\frac{n_3}{1-\pi_1-\pi_2}=0\\
\end{align}
$l_1$ and $l_2$ are the partials with respect to $\pi_1$ and $\pi_2$, respectively. The last two lines yield a useful result.
\begin{align*}
l_2&=\frac{n_1}{\pi_2}-\frac{n_1}{\pi_1}=0\\
\pi_1&=\frac{n_1}{n_2}\pi_2\\
\pi_2&=\frac{n_2}{n_1}\pi_1
\end{align*}
We use this substitution in our equation for $l_1=0$.
\begin{align*}
\frac{n_1}{\pi_1}-\frac{n_3}{1-\pi_1-\frac{n_2}{n_1}\pi_1}&=\frac{n_1}{\pi_1}-\frac{n_1\cdot n_3}{n_1-(n_1+n_2)\pi_1}=0\\
\frac{1}{\pi}&=\frac{n_3}{n_1-(n_1+n_2)\pi_1}\\
n_1-(n_1+n_2)\pi_1&=n_3\pi_1\\
\hat{\pi}_1&=\frac{n_1}{n}
\end{align*}
Similarly, we can deduce that $\hat{\pi_2}=\frac{n_2}{n}$.