AI Engineer

• Analyze requirements and design solutions
• Develop, test, and support features in production
• Own small to medium-sized features end-to-end
• Apply engineering best practices related to security, performance, scalability, and DevSecOps
• Proactively solve problems and contribute to improving existing systems
• Collaborate with teammates and contribute to the team's overall success
• Support and coach colleagues where needed

AI/Gen AI Engineer

• As our AI Engineer, your mission is to take AI models into the Yuki product and build the capabilities users actually touch.
• You get predictions and GenAI features to land robustly, performantly, and reliably in front of our accountants and entrepreneurs.
• Build GenAI capabilities: Ship compound AI systems, retrieval-augmented features, and in-product prompt orchestration.
• Productionize models: Own serving, APIs, runtime performance, and latency so AI lands reliably in the live app.
• Serve retrieval: Build in-product vector search and retrieval integration, and be first response for model-quality regressions in production.
• Land it in the product: Integrate AI predictions into the .NET backend alongside our backend engineers.
• Co-create with Product: Spot new use cases, help shape the roadmap, and build features (including MCP-based capabilities) with Product and Design.

Machine Learning Engineer

• Train, fine-tune, and ship computer-vision models for tasks like thermal anomaly detection and classification, defect detection on high-resolution imagery, object detection on drone imagery, and stitching/co-registration support.
• Level up the MLOps backbone that lets us ship reliably: experiment tracking, reproducible training, dataset versioning, a model registry, deployment pipelines, production monitoring, and a feedback loop from labeled operations data back into training.
• Run the full experimental loop end to end: curate and improve datasets, design training runs, analyse errors, and iterate.
• Take on the harder architectural problems when they matter — for example, models that reason over large spatial context (an entire site, not just a tile) where a standard fixed-resolution detector falls short.
• Integrate models into the product end to end. A model isn't done when the metric looks good — it's done when it's running on real data in the platform and making the team or the customer faster.
• Choose problems and approaches based on business impact — what actually moves the needle for our products and operations.

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AI/Gen AI Engineer

AI Engineer

AI/ML Engineer