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Applied AI Engineer
Posted 13 days agoViewed
💎 Seniority level: Middle, 4+ years
📍 Location: London, England, United Kingdom, Dubai, Dubai, United Arab Emirates
🔍 Industry: Energy
🏢 Company: Fuse Energy👥 11-50💰 $12,000,000 6 months agoElectrical DistributionRenewable EnergyEnergy
⏳ Experience: 4+ years
Requirements:
- 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.
Responsibilities:
- 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.
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