AI Lead

Jaspi is hiring an AI Lead to own how we use large language models across our hiring platform. You’ll define our model strategy, build prompt systems and evaluation loops, and stand up reliable, cost-aware AI infrastructure. If you like shipping fast, measuring rigorously, and turning research into real product value, you’ll have outsized impact here.

Jaspi ApS is a Copenhagen-founded SaaS startup building an end-to-end, AI-powered recruiting platform used by HR teams. We help companies create job ads, source candidates, run AI chat/video interviews, and score applicants transparently. We’re a small, distributed team that values speed, clarity, and a candidate-first experience—because hiring doesn’t have to be a mess.

Key Responsibilities

• Define the LLM strategy, roadmap, and success metrics across Jaspi’s product surfaces.
• Design and maintain prompt libraries with automated evaluation, guardrails, and versioning.
• Select, benchmark, and integrate foundation models; optimize for cost, latency, and quality.
• Architect scalable AI infrastructure, observability, and CI/CD for prompt and model changes.

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• Partner with product and engineering to ship features quickly and iterate safely post-release.

Required Qualifications

• Experience leading applied LLM initiatives in a production SaaS environment.
• Experience in prompt engineering, retrieval, and model fine-tuning or adaptation.

Benefits

• Remote work allowed
• Equity package
• Professional development support

In this role you’ll shape the core intelligence of a platform used by modern hiring teams. You’ll combine research-grade rigor with pragmatic shipping, influencing everything from sourcing to interview scoring while working in a tight, low-ego team.

How to Apply / What to Expect

Apply with your resume or portfolio links. We use our own AI-assisted process to run a short screening and a focused technical conversation so you get fast, consistent feedback. You can ask questions anytime; we aim for a transparent process that respects your time and evaluates mutual fit fairly.