As an AI Machine Learning Engineer, you are a key technical contributor responsible for developing and deploying complex AI initiatives. You will focus on the end-to-end lifecycle of ML solutions, from technical design and coding to production deployment and continuous optimization. This is a high-impact technical role. You will apply deep engineering rigor to build scalable, reliable systems that solve real-world R&D challenges. You don't just build models; you ensure they are integrated into robust software architectures that meet the highest standards of performance and reliability.
Responsibilities
Technical Implementation: Contribute to the design and development of 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.
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 growth.
An international, knowledgeable, and passionate team with a strong collaborative mindset
Check our LinkedIn and website to learn more about us & don’t hesitate to contact us if you have any questions.
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