Given that each move must be made in 10 seconds, here's my approach. I'm proposing that you use machine learning to evaluate moves, to approximate the exact (search-based) scoring. I'm not implementing this, but feel free to ask me for more details:

Training phase:

Create a random board configuration. Exhaustively explore the search space to find whether this is a win +1, lose -1, or draw 0 for Player 1. You now have a training example: (board configuration, game outcome)

Now, train a neural network (or other non-linear ML model, e.g. SVM) to predict the expected outcome based upon the board configuration.

Deployment phase:

Port the neural network to Javascript. For each possible move, use the neural network to predict the outcome of that move. Pick the move with highest expected outcome. The neural network will run in constant time, most likely well under 10 seconds per move.