AI Engineering & Platform Lead job opportunity at Visium.

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Date2026-03-31T21:10:28.005Z bot
Visium AI Engineering & Platform Lead
Experience: 8-years
Pattern: Full-time
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degreePhD
loacation Barcelona, Spain
loacation Barcelona....Spain
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Title: AI Engineering & Platform Lead Client: Chemical Industry Location: Barcelona About us At Visium, we partner with leading enterprises to define their AI & Data strategy, execute large- scale transformations, and integrate AI across operations, securing their organization's future. Leveraging our expertise in strategy, cloud platform engineering, and artificial intelligence, we empower our clients to unlock and scale the power of their data. We are currently spearheading a major transformation for a client in the Flavor and Fragrance sector and are seeking exceptional talent to contribute to this impactful work. This is a unique opportunity to join our client's team (as a direct employee of the client). You will shape the future of the organization, collaborating with senior leaders and cutting-edge technologies to deliver AI-driven innovation and tangible business impact. Role As the AI Engineering & Platform Lead, you serve as the central technical authority for the enterprise AI capability. Reporting to the IT Department and functionally to the Head of Digital Transformation, this foundational IT leadership role is responsible for the industrialization of AI across the organization. Your objective is to establish the infrastructure, governance, and technical talent required to scale AI initiatives. While the Digital Transformation office identifies business opportunities, defines the global strategy and manages project delivery, you own the technical execution—ensuring every solution is built on a robust enterprise platform, adheres to established engineering standards, and is managed with operational efficiency. IT & Architecture Core: You ensure the AI ecosystem is a core component of the IT landscape, integrated with enterprise network, security, and data platform architectures. Line vs. Functional Leadership: You provide Line Management for the Data Scientists and AI/ML Engineers overseeing their career progression, upskilling, and technical output quality. Functional Alignment: While technical staff report functionally to Project Managers for daily execution, you maintain authority over the technical frameworks and standards they utilize. Responsibilities Enterprise AI Infrastructure & Architecture Platform Strategy: Own the strategic roadmap and oversee the technical health of the enterprise AI platform, primarily utilizing Azure Machine Learning or Azure Databricks. Systems Integration: Ensure the AI ecosystem is integrated with the broader IT environment, specifically regarding Network architecture, Security protocols, and Enterprise Data platforms. Architectural Design: Develop reference architectures and infrastructure patterns (covering LLMs, Computer Vision, and Predictive Analytics) to provide reusable components for AI teams. Technical Governance & Standards Engineering Frameworks: Establish and enforce company-wide technical standards across the AI lifecycle, including coding practices, model versioning, and testing protocols. Compliance & Risk: Collaborate with the Security to integrate data privacy, ethical AI guardrails, and model auditing into the automated development pipelines. Technical Validation: Act as the final authority on AI deployment patterns to ensure system maintainability, security, and long-term architectural alignment. Talent Stewardship & Capability Building Line Leadership: Act as the dedicated line manager for the community of Data Scientists, and AI/ML Engineers. Professional Growth: Own the career tracks, upskilling programs, and technical certifications, ensuring the talents remain at the forefront of AI advancements. Resource Allocation: Orchestrate the assignment of technical talent into cross-functional project squads, providing the necessary technical oversight to maintain quality during delivery and technically leading some initiatives. Operational Excellence & Innovation Service Reliability: Lead the post-production support and maintenance of deployed AI products, ensuring high availability and performance against enterprise SLAs. FinOps & Observability: Optimize the AI compute footprint through cost-attribution models and advanced monitoring for model drift and system health. Technology Assessment: Monitor the AI ecosystem to evaluate emerging technologies and engage with research communities or vendors (e.g., Microsoft) to maintain institutional knowledge. Architecture & Platform Strategy Azure Management: Significant experience in the strategic oversight of Azure ML and Databricks within a large-scale, regulated corporate environment. IT Infrastructure: Technical knowledge of the intersection between AI and IT infrastructure, specifically Networking, Security, and Data Platforms. Design Proficiency: Demonstrated ability to create reference architectures for diverse AI workloads (LLMs, RAG, Predictive Analytics). Technical Validation: Experience serving as a technical gatekeeper or "Design Authority," reviewing solution architectures for maintainability, security, and scalability. Engineering & Lifecycle Expertise SDLC for AI: Comprehensive knowledge of the end-to-end AI lifecycle, including data ingestion, automated CI/CD, and production monitoring. Software Discipline: Proficiency in software engineering best practices (unit testing, modularity, version control) as applied to AI/ML. MLOps/LLMOps Standards: Experience defining and enforcing enterprise-grade operational standards for model deployment, validation, and performance tracking Lifecycle Support & Maintenance: Proven experience managing the post-production support, maintenance, and reliability of mission-critical AI systems. Leadership & Matrix Navigation Personnel Management: Proven experience in the line management of technical staff, with a focus on career development and upskilling. Stakeholder Synthesis: Ability to navigate matrix organizations and collaborate with Architecture, Security, and Digital Transformation leaders. Foundations Experience: 8+ years in IT or Engineering; 5+ years in a leadership role focused on AI platform. Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, or a related quantitative field. Languages: Professional fluency in English; proficiency in French is strongly preferred. Organizational Awareness: Clear understanding of the distinction between AI Platform management, Data Platform management, and Data Engineering/BI functions.

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