Applied AI Engineer, Fuse Energy

• Minimum 4 years of engineering experience
• Proven experience as a Backend Engineer with a strong interest and practical experience in applied AI or machine learning
• Strong programming skills in Python (or similar languages) with familiarity in AI/ML libraries (TensorFlow, PyTorch, etc.)
• Experience working with large-scale models (LLM/VLM) and deploying AI-driven solutions into production
• Solid understanding of cloud technologies, containerization, and building scalable AI applications
• Ability to integrate AI/ML models into real-world applications, focusing on usability and performance
• Strong problem-solving skills and a practical approach to implementing AI solutions in a fast-paced environment
• Familiarity with cloud-based platforms (AWS is a plus) and services related to AI/ML is a plus
• Experience or strong interest in energy markets and trading strategies
• Understanding of weather forecasting, energy demand patterns, and production modelling
• Experience working with large datasets, particularly in relation to demand and supply forecasting

Desirable

• We are looking for an Applied AI Engineer to join our growing team at Fuse Energy
• This position is ideal for an engineer 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
• As an Applied AI Engineer, you will work on a variety of exciting projects, including consumer-focused features like the Energy Co-pilot and the Speedy Onboarding process (leveraging tools such as VLM/LLM)
• You will also collaborate across teams to build AI tools that enhance productivity and streamline processes within Fuse Energy
• Design, develop, and deploy AI-powered features that directly impact consumer experiences, including personalised energy recommendations and seamless onboarding via AI models (e.g., using energy bills for quick setup)
• Build and optimise internal AI tools that will make the whole company more productive, with a focus on automation and enhancing workflows
• Collaborate with backend engineers and data scientists to integrate AI-driven features into our platforms
• Continuously improve and optimise AI models (including LLM and VLM) to provide a better user experience
• Develop scalable, maintainable AI infrastructure to support a growing set of consumer-facing and internal AI features
• Collaborate with the trading and operations teams to ensure the AI models are aligned with real-time market conditions and energy pricing
• Improve AI models to optimise trading strategies by anticipating market shifts based on weather and demand forecasts
• Stay up to date with the latest advancements in applied AI and machine learning, and apply them to solve real-world problems within the energy space
• Monitor the performance of AI tools and models, ensuring they are functioning efficiently and effectively