AI Engineer

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 an Senior AI Engineer! 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, establish best practices, and ensure AI systems are scalable, observable, and production-grade. Responsibilities AI Engineering & Architecture - 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 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 and maintain reproducible ML pipelines and CI/CD workflows for AI components - Manage 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 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 Qualifications - Bsc/Master’s degree in Computer Science, Machine Learning, Data Science, or a related field. - 5+ years of professional software engineering experience, including shipping and operating cloud services in production - Hands-on experience in LLM-powered production applications or ML/NLP applications. - Strong proficiency in 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-on MLOps experience: 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 quickly identify regressions - Proven experience with AWS and cloud-based AI deployments. - Strong 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 fast-paced, collaborative environment. Strong Plus - Strong AWS (or equivalent cloud platform) experience 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 enterprise or B2B environments.