Senior AI Engineer

Nexthink is the leader in digital employee experience management software. The company provides IT leaders with unprecedented insight allowing them to see, diagnose and fix issues at scale impacting employees anywhere, with any applicationor network, before employees notice the issue. As the first solutionto 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 millionemployees. 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 asanSeniorAIEngineer

As a senior member of the AI team, you will prototype, mature, and ship AI-powered capabilities into Nexthink’s cloud platform. You will lead architectural decisions,establishbest practices, and ensure AI systems are scalable, observable, and production-grade.

Responsibilities

AI Engineering & Architecture

• Design, develop, andoperateproduction-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

AI Done Right – Evaluation & Quality

• 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& Cloud Engineering

• Implement andmaintainreproducible ML pipelines and CI/CD workflows for AI components
• Manage deployment, monitoring, and lifecycle of models and AI artifacts in production
• Optimizesystems 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, testable engineering plans
• Proactively propose new AI capabilities based on user insights and technology advancements
• Communicate complex AI concepts clearly to both technical and non-technical stakeholders

Leadership & Mentorship

• Mentor and coach junior AI engineers in production best practices
• Establish engineering standards and AI best practices within the team
• Foster a culture of experimentation, learning, and knowledge sharing

Strong Plus

• Bsc/Master’s degree in Computer Science, Machine Learning, Data Science, ora relatedfield.
• 5+ years of professional software engineering experience, including shipping and operating cloud services in production
• Hands-on experience inLLM-powered production applicationsor ML/NLP applications.
• Strongproficiencyin Python and AI frameworks
• Strong 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 integrating and operating LLMs (prompting, evaluation, observability, RAG, agentic workflows)
• Hands-onMLOpsexperience: 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.
• Strong data intuition: ability to inspect logs, design metrics, and quicklyidentifyregressions
• Proven experience with AWS and cloud-based AI deployments.
• Strong communicationskills in English, capable of explaining complex AI concepts to technical and non-technical stakeholders
• Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.

Ifyou’reexcited about pushing the boundaries of AI and mentoring the next generation of engineers,we’dlove to hear from youEven if youdon’tmeet every requirement, we encourage