AI Machine Learning Engineer
Role details
Job location
Tech stack
Job description
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
Requirements
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. Requirements 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 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 J-18808-Ljbffr