Nexthink Deutschlandweit vor 8 Monaten

Senior AI Engineer

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Das ist der Job

Nexthink is the leader in digital employee experience management software.

Darum lohnt es sich

As a senior member of the AI team, you will prototype, mature, and ship AI-powered capabilities into Nexthink’s cloud platform. The company provides IT leaders with unprecedented insight allowing them to see, diagnose and fix issues at scale impacting employees anywhere, with any application or network, before employees notice the issue.

As the first solution to allow IT to progress from reactive problem solving to proactive optimization, Nexthink enables its more than 1,300 customers to provide better digital experiences to more than 18 million employees.

Dual headquartered in Lausanne, Switzerland and Boston, Massachusetts, Nexthink has 9 offices worldwide. #LI-Hybrid Are you passionate about AI and eager to drive innovation in a fast-paced, impact-driven environment? Do you have experience developing AI-powered applications and enjoy mentoring others?

If so, we invite you to join Nexthink as a Senior AI Engineer! You will lead architectural decisions, establish best practices, and ensure AI systems are scalable, observable, and production‑grade.

Responsibilities Design, develop, and operate production‑grade AI/ML systems, including LLM‑powered applications, NLP models, RAG pipelines, and multi‑agent systems Make key architectural decisions across model selection, training strategies, fine‑tuning, retrieval mechanisms, orchestration layers, and infrastructure Integrate external AI services (e.g., LLM providers) into Nexthink’s cloud platform Solve engineering challenges related to data collection, retrieval, evaluation, inference, latency, and cost optimization Define robust online and offline evaluation frameworks and success metrics Instrument dashboards and monitoring systems to track quality and detect regressions in production Design automated evaluation pipelines for prompts, embeddings, models, and agent workflows Ensure observability and reliability of AI systems at scale MLOps

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