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.

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 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.

Required Skill Profession

Computer Occupations

📌 Senior AI Machine Learning Engineer (Barcelona)
🏢 Visium
📍 Barcelona