AI Machine Learning Engineer

and model observability. - Agile Delivery: Work within an Agile framework to ensure research translates into predictable production value, meeting project milestones and deadlines. - Business Advisory: Partner with technical and business stakeholders to translate business challenges into technical requirements and clear project updates. Who you are You are passionate about AI and driven to deliver real-world impact through data. You thrive in R&D-heavy environments involving sparse or high-dimensional data, excelling at the intersection of experimental AI research and disciplined software engineering. You are a clear communicator who can explain technical trade-offs to both engineering peers and business stakeholders. Advanced AI/ML Engineering & Software Craftsmanship - Production-Level Programming: Senior proficiency in Python, with a strong commitment to software engineering best practices (Design Patterns, Unit Testing, and Modular Code). - System Design: Solid understanding of

modern AI/ML architectures and data platforms to build robust, performant systems. - Modeling Depth: Deep knowledge of AI/ML algorithms and the mathematical foundations required to tune models for high-precision R&D use cases. - Data Engineering: Proficiency in handling data structures and pipelines to ensure model inputs are reliable and optimized. Advanced MLOps & Cloud Infrastructure - Azure: Hands-on experience with the Azure ML SDK/CLI or Azure Databricks, including managed online endpoints, compute clusters, and data assets. - CI/CD: Experience building and maintaining deployment pipelines using Azure DevOps or automation in GitLab. - Containerization: Proficiency in Docker for packaging and scaling AI/ML workloads within cloud-native environments. - Observability & Reliability: Ability to implement monitoring for system health (latency/CPU) and model performance (drift, accuracy, and data quality). Professional Collaboration - Agile Methodology: Experience working within an Agile/Scrum framework to deliver consistent project velocity. - Technical Translation: Ability to communicate complex trade-offs clearly to non-technical stakeholders. - Project Delivery: Proven track record of taking ML models from a research phase to a stable production environment. Contextual Plus - Academic Background: Master's degree or higher in Computer Science, AI, Data Science, or a related field. - Domain Expertise (Preferred): Exposure to formulation, chemistry or the Fragrance & Flavour industry. - Languages: Full professional proficiency in English; French is strongly preferred. What we offer - A competitive compensation package - A yearly education budget to steep your learning curve - A yearly sport budget because a fit body leads to a fit mind - A flexible working culture because your work-life balance matters to us - A position that enables you to have an impact on 1,000s of people, and the whole company's