Strong programming skills in Python (or similar languages) with familiarity in AI/ML libraries (TensorFlow, PyTorch, etc.)
Understanding of weather forecasting, energy demand patterns, and production modelling
Solid understanding of cloud technologies, containerisation and building scalable AI applications
Strong problem-solving skills and a practical approach to implementing AI solutions in a fast-paced environment
Experience working with large-scale models (LLMs/VLMs) and deploying AI-driven solutions into production
Minimum 3 years of engineering experience
Proven experience as a Backend Engineer with a strong interest and practical experience in applied AI or machine learning
Experience working with large datasets, particularly in relation to demand and supply forecasting
Exposure to Natural Language Processing (NLP) or other related fields
Ability to integrate AI/ML models into real-world applications, focusing on usability and performance
As an Applied AI Engineer, this position is ideal for someone who possesses the technical expertise of a backend engineer but is specifically interested in applied AI and how it can be used to enhance the energy experience for our customers and our internal operations
Experience or strong interest in energy markets and trading strategies

Design, develop, and deploy AI-powered features that directly impact consumer experiences, including personalized energy recommendations and seamless onboarding via AI models.
Collaborate with backend engineers and data scientists to integrate AI-driven features into our platforms, ensuring alignment with real-time market conditions and energy pricing.
Stay up to date with the latest advancements in applied AI and machine learning, applying them to solve real-world problems within the energy space.

Applied AI Engineer

Summary