Applied AI Engineer

In this role you will build and operationalize applied AI features that enhance the energy experience for customers and internal teams. You will own AI-powered workflows end-to-end, from design to production, shaping how Fuse uses AI across trading, operations, and customer support. You’ll deploy and monitor models, align them with real-time market and weather data, and ensure reliability in production. This position offers the chance to impact energy accessibility at scale within a fast-moving startup culture.

• competitive salary
• equity eligibility
• biannual bonus scheme
• fully expensed tech
• private health insurance
• breakfast and dinner allowance

• Ship AI features for customers (e.g., personalized energy recommendations and onboarding from energy bills)
• Build LLM applications and agents for support, engineering, warehouse and back-office teams, including multi-agent systems and durable workflows (e.g., Temporal)
• Develop the surrounding engineering—cloud infrastructure, integrations, and access control for secure usage
• Own evaluation processes: datasets, golden answers, human and LLM judges, A/B tests
• Select models based on performance, latency and cost, and update as models improve
• Collaborate with trading, operations and backend teams to reflect real-time market, pricing, demand and weather forecasts
• Monitor AI systems in production and maintain reliability

• 3+ years of relevant experience with role level varying
• Experience owning an applied AI system end-to-end in a startup or small team
• Background as software engineer transitioning to applied AI, or ML engineer writing production code
• Strong Python and production deployment of LLMs or VLMs
• Solid understanding of cloud technologies, containerization, and scalable AI applications
• Analytical, rigorous, quick to learn; able to pick up new tools rapidly
• Bonus: PyTorch or TensorFlow; forecasting/weather/demand modelling; energy markets and trading; fine-tuning or RL; NLP/vision pipelines; agent runtimes and MCP

• analytical mindset
• rigor and detail orientation
• quick learner
• Python production code
• LLM/VLM deployment
• cloud technologies