Reinforcement Learning Engineer

Key Responsibilities Design, implement, and fine-tune reinforcement learning algorithms (e.g., Q-learning, PPO, DDPG, Policy Gradients). Build robust environment simulations and custom reward structures for model training. Collaborate with data science and engineering teams to integrate RL models into production systems. Evaluate model performance, analyze failure cases, and iteratively improve algorithmic accuracy and efficiency. Stay updated with current industry research and developments in Deep Learning and RL. Required Qualifications & Skills Strong foundation in Machine Learning, Deep Learning, and Reinforcement Learning concepts. Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow). Experience with RL libraries such as Stable-Baselines3, Ray RLlib, or OpenAI Gym. Solid understanding of probability, linear algebra, and optimization algorithms. Good problem-solving skills and the ability to work effectively in a team environment.