Senior AI Machine Learning Engineer
Indexed description
- Title: Senior AI Machine Learning Engineer
- Client: Chemical Industry
- Location: Barcelona
Role
As a Senior AI/ML 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
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
- 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)
- 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
- 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
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