3+ years of experience in applied AI or related fields.
Proven track record of owning an applied AI system from start to finish.
Background as a software engineer or ML engineer with production coding experience.
Strong proficiency in Python or similar programming languages.
Familiarity with cloud technologies and scalable AI applications.
Analytical mindset with a quick learning ability.
Bonus: Experience with PyTorch or TensorFlow, energy markets, and advanced AI techniques.
Responsibilities
Develop and launch AI features for personalized customer energy recommendations.
Create LLM applications for various internal teams, including support and engineering.
Build and maintain cloud infrastructure and integrations for AI systems.
Conduct evaluations using datasets and A/B tests to assess AI performance.
Select AI models based on performance metrics and cost considerations.
Collaborate with trading and operations teams to ensure models reflect real-time data.
Monitor and ensure the reliability of AI systems in production.
Benefits
Competitive salary and eligibility for equity.
Biannual bonus scheme.
Fully expensed tech tailored to your needs.
Private health insurance.
Breakfast and dinner allowance for office-based employees.
Full Job Description
As an Applied AI Engineer, this role suits someone who is specifically interested in applied AI and how it can improve the energy experience for our customers and our internal operations.
You'll work on consumer features (using VLM and LLM tools), and build AI tools and agents that make teams across Fuse more productive, from customer support to warehouses and back office. This is an applied engineering role, not pure research: you'll own what you build, from spec to production.
Responsibilities
Ship AI features for customers, such as personalised energy recommendations and onboarding from an energy bill.
Build LLM applications and agents for support, engineering, warehouse and back-office teams, including multi-agent systems and durable workflows (e.g. Temporal).
Build the engineering around them: cloud infrastructure, integrations, and access control so users and agents only reach what they should.
Own evaluation: datasets, golden answers, human and LLM judges, A/B tests. Know whether a change genuinely helped.
Choose models by performance, latency and cost, and revisit as models improve.
Work with trading, operations and backend teams so models reflect real-time market conditions, energy pricing and demand and weather forecasts.
Monitor AI systems in production and keep them reliable.
Requirements
3+ years of experience, with requirements varying by role level.
Experience owning an applied AI system end to end, ideally in a startup or small team, and able to explain what broke and how you knew it worked.
A software engineer who moved into applied AI, or an ML engineer who writes production code. We value both.
Strong Python (or similar) and experience deploying LLMs or VLMs into production.
Solid understanding of cloud technologies, containerisation and scalable AI applications.
Analytical, rigorous and quick to learn. You don't need our exact tools, just the ability to pick them up.
Bonus: PyTorch or TensorFlow; forecasting, weather and demand modelling; energy markets and trading; fine-tuning or RL; vision, document or NLP pipelines; agent runtimes and MCP.
Benefits
Competitive salary and eligibility for equity
Biannual bonus scheme
Fully expensed tech to match your needs
Private health insurance
Breakfast and dinner allowance for office-based employees
As we hire globally, benefits may vary by location.
