Machine Learning Senior Engineer

Hace 3 semanas
Barcelona, Barcelonés (comarca); Provincia de Barcelona; Cataluña, España Visium SA Jornada completa
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. You will apply deep engineering rigor to build scalable, reliable systems that solve real-world R&D challenges. you ensure they are integrated into robust software architectures that meet the highest standards of performance and reliability. Deliver high-quality code for data, modelling, and deployment pipelines, leading the team through engineering rigor. 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. 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 Craftsmanship Production‐Level Programming: Senior proficiency in Python, with a strong commitment to software engineering best practices (Design Patterns, Unit Testing, and Modular Code). Solid understanding of modern AI/ML architectures and data platforms to build robust, performant systems. 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. Proficiency in Docker for packaging and scaling AI/ML workloads within cloud‐native environments. 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. 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. Languages: Full professional proficiency in English; French is strongly preferred. 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

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