AI/ML Engineer - Reinforcement Learning

Our client, a global leader in developing innovative AI solutions, is seeking a highly skilled AI/ML Engineer specializing in Reinforcement Learning to join their team in **Kolkata, West Bengal, IN**. This role is crucial for advancing Our client's capabilities in creating intelligent systems that can learn and adapt through interaction with their environment. You will be responsible for designing, implementing, and deploying sophisticated RL algorithms for complex decision-making problems across various domains. The ideal candidate possesses a strong theoretical understanding of RL concepts and practical experience in applying them to real-world challenges. Join a forward-thinking team in **Kolkata** that is pushing the boundaries of AI and making a significant impact. This is a unique opportunity to work on cutting-edge RL research and development in a thriving technological ecosystem.

Develop and implement advanced Reinforcement Learning algorithms (e.g., Q-learning, Policy Gradients, Actor-Critic methods). Design and train RL agents for complex control, optimization, and decision-making tasks. Work with simulation environments and real-world data to train and evaluate RL models. Collaborate with other engineers and researchers to integrate RL solutions into larger systems. Optimize RL algorithms for performance, stability, and scalability. Stay current with the latest research trends and breakthroughs in Reinforcement Learning and AI. Contribute to the codebase, ensuring high-quality, maintainable, and well-documented software. Analyze experimental results and provide actionable insights. Explore novel applications of RL in various industries.

Master's or Ph.D. in Computer Science, Artificial Intelligence, Robotics, or a related field with a specialization in Reinforcement Learning. 2+ years of practical experience in applying Reinforcement Learning techniques. Strong programming skills in Python and experience with ML libraries (e.g., TensorFlow, PyTorch, OpenAI Gym, Stable Baselines). Solid understanding of Markov Decision Processes, value functions, and policy optimization. Experience with deep learning architectures relevant to RL (e.g., DCNs, RNNs). Familiarity with simulation tools and environments is a plus. Excellent analytical and problem-solving capabilities. Strong communication skills and the ability to work effectively in a team.