Applied AI Engineer
About The Position
Fuse Energy is an energy startup working to make energy abundant and affordable. The company is building an integrated energy business spanning generation projects, hardware, grid infrastructure, real-time power trading, AI, and distributed energy installations in homes. The Applied AI Engineer will develop applied AI capabilities that improve the energy experience for customers and increase productivity across internal teams. The work includes consumer-facing features using vision-language and large language models, as well as AI tools and agents for teams such as customer support, engineering, warehouse, and back office. This is an applied engineering role rather than a pure research position, with ownership of projects from specification through production.
Requirements
- At least 3 years of experience; requirements vary by role level.
- Experience owning an applied AI system end to end, ideally in a startup or small team, and ability to explain what broke and how its effectiveness was assessed.
- Background as either a software engineer who moved into applied AI or an ML engineer who writes production code.
- Strong Python or similar programming skills and experience deploying LLMs or VLMs into production.
- Solid understanding of cloud technologies, containerisation, and scalable AI applications.
- Analytical, rigorous, and quick to learn.
Nice To Haves
- Experience with PyTorch or TensorFlow.
- Experience with forecasting, weather, or demand modelling.
- Knowledge of energy markets and trading.
- Experience with fine-tuning or reinforcement learning.
- Experience with vision, document, or NLP pipelines.
- Experience with agent runtimes and MCP.
Responsibilities
- Ship customer-facing AI features, such as personalised energy recommendations and onboarding based on an energy bill.
- Build LLM applications and agents for support, engineering, warehouse, and back-office teams, including multi-agent systems and durable workflows such as Temporal.
- Build the supporting engineering systems, including cloud infrastructure, integrations, and access controls for users and agents.
- Own evaluation through datasets, golden answers, human and LLM judges, and A/B tests to determine whether changes are effective.
- Select models based on performance, latency, and cost, and revisit choices as models improve.
- Work with trading, operations, and backend teams so models reflect real-time market conditions, energy pricing, demand, and weather forecasts.
- Monitor AI systems in production and maintain their reliability.
Benefits
- Competitive salary and eligibility for equity.
- Biannual bonus scheme.
- Fully expensed technology to match employee needs.
- Private health insurance.
- Breakfast and dinner allowance for office-based employees.
- Benefits may vary by location.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Education Level
No Education Listed
