Senior AI Engineer

• 30 June 2026 • 100% • Indeterminate duration • Lausanne Senior AI Engineer • Full-time • Department: Diagnostics • Dept: Diagnostics • Division: Engineering Company Description Nexthink is the leader in digital employee experience software. The company provides IT leaders with unprecedented visibility to see, diagnose, and resolve at scale the problems impacting employees everywhere, with any application or network, before employees notice the problem. As the first solution enabling IT to shift from reactive problem-solving to proactive optimization, Nexthink allows more than 1,300 clients 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-Hybride # 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 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 in Nexthink's cloud platform. You will drive architectural decisions, establish best practices, and ensure that AI systems are scalable, observable, and production-ready. Responsibilities AI Engineering & Architecture Design, develop, and operate high-quality production 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 Nexthink's cloud platform Solve engineering challenges related to data collection, retrieval, evaluation, inference, latency, and cost optimization AI Done Right - Evaluation & Quality Define robust online and offline evaluation frameworks as well as success metrics Set up dashboards and monitoring systems to track quality and detect regressions in production Design automated evaluation pipelines for prompts, embeddings, models, and agent workflows Ensure the observability and reliability of large-scale AI systems MLOps & Cloud Engineering Implement and maintain reproducible ML pipelines and CI/CD workflows for AI components Manage the deployment, monitoring, and lifecycle of models and AI 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 leaders, 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 Communicate complex AI concepts clearly to technical and non-technical stakeholders Leadership & Mentorship Mentor and coach junior AI engineers on production best practices Establish engineering standards and best practices for AI within the team Foster a culture of experimentation, learning, and knowledge sharing Qualifications Bachelor's/Master's degree in Computer Science, Machine Learning, Data Science, or related field. More than 5 years of professional experience in software engineering, including deploying and operating cloud services in production Practical experience with production-ready 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 with integrating and operating LLM (prompting, evaluation, observability, RAG, agent workflows) Practical 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 English communication skills, able to explain 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 optimizing models for latency, throughput, and cost. Experience fine-tuning large language models. Familiarity with multi-agent systems and orchestration frameworks. Experience designing AI systems in a production environment