Reinforcement Learning Engineer

Develop and implement reinforcement learning algorithms and models. Design environments, agents, rewards, and training strategies. Train and evaluate RL models using real-world and simulated data. Implement algorithms such as Q-Learning, DQN, PPO, A2C, and Actor-Critic. Perform experimentation, hyperparameter tuning, and model optimization. Use simulation environments to test and validate intelligent agents. Analyze model performance and improve learning efficiency. Develop scalable machine learning pipelines for RL applications. Collaborate with data scientists, software engineers, and research teams. Deploy trained models into production applications. Maintain technical documentation and experiment results. Research new reinforcement learning techniques and industry developments.