Position: AI/ML Engineer Reinforcement Learning
Location: [Remote] Duration: 14 months Contract
Job Description
We are looking for an experienced AI/ML Engineer with strong hands-on experience in Machine Learning, Deep Learning, and Reinforcement Learning (RL). The ideal candidate will design, develop, train, evaluate, and deploy intelligent models that learn from data, feedback, and interactions with dynamic environments.
The candidate should have strong knowledge of RL algorithms, ML model development, Python, and modern AI/ML frameworks, along with experience applying reinforcement learning to real-world business or technical problems.
Responsibilities
- Design and develop Machine Learning and Reinforcement Learning solutions for real-world applications.
- Develop, train, evaluate, and optimize RL agents in simulated or real-world environments.
- Define and implement states, actions, rewards, policies, and environments for RL problems.
- Implement and experiment with algorithms such as Q-Learning, SARSA, DQN, PPO, A2C/A3C, SAC, and other RL approaches.
- Select appropriate RL algorithms based on action space, environment, data availability, and business objectives.
- Develop ML/DL models using Python, PyTorch, TensorFlow, or similar frameworks.
- Design reward functions and address challenges such as sparse rewards, reward shaping, exploration vs. exploitation, and reward hacking.
- Build simulation environments and training pipelines for RL agents.
- Perform model evaluation using appropriate metrics such as cumulative reward, success rate, convergence, and business KPIs.
- Optimize model performance, training stability, and computational efficiency.
- Work with data scientists, ML engineers, software engineers, and business stakeholders to integrate AI/ML solutions.
- Develop APIs and services for deploying trained models into production environments.
- Monitor deployed models and analyze model performance and behavior.
- Conduct experiments, document findings, and communicate technical results to stakeholders.
- Participate in code reviews, architecture discussions, and Agile development processes.
Required Skills
- 15 years of experience in AI/ML, Machine Learning, or Data Science.
- Strong programming experience with Python.
- Strong understanding of Reinforcement Learning concepts, including:
- Markov Decision Processes (MDPs)
- State and action spaces
- Reward functions
- Policy and value functions
- Q-functions
- Bellman equations
- Exploration vs. exploitation
- Discount factor
- Hands-on experience with RL algorithms such as:
- Q-Learning
- SARSA
- DQN
- PPO
- A2C/A3C
- SAC
- Strong experience with PyTorch and/or TensorFlow.
- Experience with Deep Learning and Neural Networks.
- Strong knowledge of ML model training, evaluation, and optimization.
- Experience working with Gym/Gymnasium, Stable-Baselines3, Ray/RLlib, or similar RL frameworks.
- Experience designing and implementing RL environments, states, actions, and reward functions.
- Strong understanding of Supervised Learning, Unsupervised Learning, and Deep Learning.
- Experience with data preprocessing, feature engineering, and model evaluation.
- Strong understanding of Python ML libraries such as NumPy, Pandas, and Scikit-learn.
Preferred Skills
- Experience applying Reinforcement Learning to optimization, recommendation, robotics, gaming, autonomous systems, resource allocation, or supply chain problems.
- Experience with Generative AI, LLMs, or RAG is a plus.
- Knowledge of Deep Reinforcement Learning.
- Experience with simulation environments.
- Experience with distributed model training.
- Experience with Docker and Kubernetes.
- Experience deploying ML models on AWS, Azure, or Google Cloud Platform.
- Knowledge of MLflow, Kubeflow, or similar MLOps platforms.
- Experience building production-grade ML pipelines.
- Knowledge of model monitoring and observability
Thanks and Regards,
Pooja Jaiswal Technical Recruiter
Aptino, Inc.
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