AI/ML Engineer

scalable, maintainable AI solutions aligned with modern best practices.Hands-On Development: Deliver high-quality code for data, modelling, and deployment pipelines, leading the team through engineering rigor.MLOps Mastery: Implement and maintain robust MLOps workflows, focusing on automated CI/CD, containerization, 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 CraftsmanshipProduction-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 InfrastructureAzure: 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 Dev Ops or automation in Git Lab.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 CollaborationAgile 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 PlusAcademic 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 offerA competitive compensation packageA yearly education budget to steep your learning curveA yearly sport

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