Applied AI Engineer

Applied AI Engineer focuses on build evaluation alongside the feature, not after it – test against real business cases, measure quality honestly, and let the numbers settle arguments.

What the role involves

• Build evaluation alongside the feature, not after it – test against real business cases, measure quality honestly, and let the numbers settle arguments.
• Handling the unglamorous parts well: error handling, fallbacks when a model misbehaves, latency, token cost, logging and monitoring.
• Work with the engineers who own each codebase, fitting in with their patterns and pipelines rather than parachuting in something nobody else can maintain.
• Prompt engineering and context design patterns.
• A track record of shipping quickly, with examples of taking something from idea to working software in weeks rather than quarters.
• Delivering AI features end-to-end: from requirement understanding through to shipped, evaluated product capability.

Skills and requirements

• Experience modernising or extending long-lived systems, in .NET or elsewhere.
• Test datasets or automated quality pipelines for AI outputs.

Confirmed role details

• "At Klipboard we've introduced a flexible hybrid work policy, where employees spend three days in the office and two days working from home.
• This approach promotes a balanced work environment that combines office collaboration with the comfort and convenience of remote work.".

Candidate fit

• technical judgement, safe working habits, careful diagnostics, and practical problem-solving