Senior AI Engineer

• 31 juillet 2026 • 100% • Indefinite duration • Lausanne Senior AI Engineer • Full time • Department: Diagnostics • Dept: Diagnostics • Division: Engineering Company Description Nexthink is the leader in employee digital experience management software. The company offers IT managers unprecedented visibility allowing them to see, diagnose, and resolve at scale the issues impacting employees everywhere, with any application or network, before employees notice the problem. As the first solution enabling IT to move from reactive problem solving to proactive optimization, Nexthink enables its more than 1,300 customers to offer better digital experiences to more than 18 million employees. Based in both Lausanne, Switzerland, and Boston, Massachusetts, Nexthink has 9 offices worldwide. LI-Hybrid # Job Description Are you passionate about AI and eager to drive innovation in a dynamic and impact-driven environment? Do you have experience in developing AI-powered applications and do you enjoy mentoring others? If so, we invite you to join Nexthink as a Senior AI Engineer! As a senior member of the AI team, you will prototype, develop, and deploy AI-powered capabilities within the Nexthink cloud platform. You will lead architectural decisions, establish best practices, and ensure that AI systems are scalable, observable, and production-ready. Responsibilities AI Engineering & Architecture Design, develop, and operate production-quality AI/ML systems, including LLM-powered applications, NLP models, RAG pipelines, and multi-agent systems Make key architectural decisions regarding model selection, training strategies, fine-tuning, retrieval mechanisms, orchestration layers, and infrastructure Integrate external AI services (e.g., LLM providers) into the Nexthink cloud platform Solve engineering challenges related to data collection, retrieval, evaluation, inference, latency, and cost optimization AI Done Well - Evaluation & Quality Define robust online and offline evaluation frameworks as well as success metrics Implement 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 & Cloud Engineering Implement and maintain reproducible ML pipelines and CI/CD workflows for AI components Manage the deployment, monitoring, and lifecycle of AI models and artifacts in production Optimize systems for scalability, performance, throughput, and cost Work with AWS (or equivalent cloud platforms), Docker, and orchestration frameworks (Kubernetes/ECS) Product & Cross-functional Collaboration Collaborate closely with product managers, designers, software engineers, and data scientists Translate ambiguous product requirements into incremental and testable engineering plans Proactively propose new AI capabilities based on user insights and technological advancements Clearly communicate complex AI concepts to technical and non-technical stakeholders Leadership & Mentoring Mentor and coach junior AI engineers on production best practices Establish engineering standards and AI best practices within the team Foster a culture of experimentation, learning, and knowledge sharing Qualifications Bachelor's/Master's in computer science, machine learning, data science, or a related field. More than 5 years of professional experience in software engineering, including production deployment and operation of cloud services Hands-on experience in production applications powered by LLM or ML/NLP applications. Proficiency in Python and AI frameworks Good understanding of machine learning fundamentals (supervised/unsupervised learning, optimization, model evaluation). Solid understanding of machine learning fundamentals (training, optimization, evaluation) Experience with NLP systems (embeddings, semantic search, retrieval systems, text classification, etc.) Experience in integrating and operating LLMs (prompting, evaluation, observability, RAG, agentic workflows) Hands-on experience in MLOps: reproducible pipelines, experiment tracking, automated evaluation, CI/CD for models and prompts Knowledge of reinforcement learning, retrieval-augmented generation (RAG), and multi-agent AI architectures. Good data intuition: ability to inspect logs, design metrics, and quickly identify regressions Proven experience with AWS and cloud-based AI deployments. Excellent communication skills in English, capable of explaining complex AI concepts to technical and non-technical stakeholders Excellent problem-solving skills and ability to work in a dynamic and collaborative environment. Assets Solid experience with AWS (or equivalent cloud platform) for scalable AI infrastructure. Experience in optimizing models for latency, throughput, and cost. Experience in fine-tuning large language models. Familiarity with multi-agent systems and orchestration frameworks. Experience in designing AI systems in an enterprise or B