Description
- Develop and deploy AI-based solutions to automate engineering and business workflows, improving efficiency, decision-making, and productivity
- Identify opportunities to replace manual or rule-based processes with intelligent AI-driven workflows and agents
- Design, develop, and integrate machine learning (ML), reinforcement learning (RL), and generative AI models for engineering applications
- Replace conventional rule-based control algorithms and physics-based functions with data-driven ML/RL models where appropriate
- Develop data pipelines for collection, cleaning, feature engineering, training, validation, and deployment of AI models
- Train, optimize, and validate ML/RL models using simulation, test, and field data
- Collaborate with controls, software, systems, and domain experts to integrate AI models into production systems
- Support development of digital twins, predictive analytics, anomaly detection, optimization, and intelligent decision-making systems
- Monitor model performance, perform retraining activities, and ensure robustness and scalability of deployed solutions
- Prepare technical reports, documentation, presentations, and demonstrations for internal and customer stakeholders
- Stay current with emerging AI technologies, frameworks, and best practices and evaluate their applicability to engineering challenges
Requirements
- Working towards Bachelor’s or master’s degree in computer science, Electrical Engineering, Mechanical Engineering, Robotics, Data Science, Artificial Intelligence, or a related field
- Understanding of Machine Learning, Deep Learning, Reinforcement Learning, and Generative AI concepts
- Proficiency in Python and common AI/ML frameworks such as TensorFlow, PyTorch, and RL libraries
- Experience with software development tools, version control systems, and CI/CD processes
- Experience with data processing, feature extraction, model training, validation, and deployment workflows
Preferred Qualifications:
- Knowledge of optimization techniques, control systems, and system modeling concepts
- Familiarity with cloud-based AI platforms and MLOps practices is preferred
- Strong analytical and problem-solving skills with the ability to work on complex engineering challenges
- Professional communication skills (oral and written) and ability to present technical concepts to diverse audiences