Applied AI Engineer

Fuse Energy

. Ship AI features for customers, such as personalised energy recommendations and onboarding from an energy bill .

Posted 11/5/2026full-timeRemote • United Kingdom, United States, Switzerland, United Arab Emirates, GermanyMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying AI applications, particularly in cloud environments, with a strong focus on performance evaluation and real-time market integration. Proficient in Python and familiar with LLMs, containerization, and scalable AI solutions.

Highest-signal resume keywords

Applied AI System OwnershipPython ProgrammingLLM DeploymentCloud TechnologiesEnergy Markets Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Applied AIMachine Learning EngineeringProduction Code WritingData AnalysisModel EvaluationPerformance OptimizationForecastingDemand ModellingNLP PipelinesAgent Runtimes

Soft Skills

Analytical ThinkingQuick LearnerCollaboration

Tools & Technologies

PyTorchTensorFlowCloud InfrastructureContainerizationTemporal

Industry Keywords

Energy RecommendationsMarket ConditionsWeather ForecastsA/B TestingMulti-Agent Systems

Tech Stack

Tools & technologies

CloudPythonPyTorchTensorflow

About the role

Key responsibilities & impact

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 such as Temporal
Build cloud infrastructure, integrations and access control around AI applications
Own evaluation through datasets, golden answers, human and LLM judges, and A/B tests
Choose models based on performance, latency and cost, and revisit choices as models improve
Collaborate 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

Requirements

What you’ll need

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
Software engineering experience transitioned into applied AI, or ML engineering experience writing production code
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; ability to pick up new tools
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

Comp & perks

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
Benefits may vary by location