Are you interested in leading Artificial Intelligence projects that solve real industrial problems and generate measurable value for companies?

The Applied Artificial Intelligence Unit at EURECAT is looking for an experienced AI scientist and project leader to lead industrial and R&D; projects involving the development, validation and deployment of advanced AI and data-driven solutions for the manufacturing sector.

We work at the intersection of advanced AI research and industrial implementation. We collaborate directly with manufacturing companies and other industrial organizations, as well as with technology centres, universities and partners within competitive national and European R&D; programmes.

Our projects address challenges such as Industrial Digital Twins, process monitoring, quality prediction, anomaly detection, process optimisation, predictive maintenance, decision support, resource efficiency and increasingly autonomous industrial operations. We work with technologies ranging from Machine Learning, Deep Learning and industrial time-series analytics to surrogate modelling, physics-informed AI, Generative AI, LLMs and Agentic AI. Technology selection will always be driven by the industrial problem, available data, project constraints and expected customer value rather than by the technology itself.

This position has a strong focus on project leadership, customer value and industrial applications. We are looking for someone capable of understanding how an industrial operation works, identifying where AI can realistically create value, structure a technically and economically sound project, and leading and participating in its execution towards a successful delivery.

You will be responsible not only for defining and creating technically sound solutions,



but also for ensuring that projects remain aligned with the customer’s operational needs, expected impact, scope, schedule and available resources. This requires regular interaction with industrial customers, project partners and technical teams. You will help translate operational challenges into feasible AI projects, establish priorities, manage project execution and ensure that the solutions developed provide tangible value

A strong understanding of AI and data science remains important. You should have sufficient hands‑on experience to participate in technical developments, and assess technical alternatives, challenge technical decisions and understand the limitations and risks of the proposed approaches.

RESPONSIBILITIES

Project leadership and delivery: Lead AI R&D; projects for industrial manufacturing from initial definition through execution, validation and final delivery. Coordinate project scope, activities, resources, schedules, milestones, deliverables, dependencies and risks, ensuring that projects remain technically feasible and aligned with customer expectations.

Customer value and impact: Maintain a clear focus on the value generated for customers and partners. Define expected project outcomes and success criteria together with stakeholders and continuously assess whether the technical work is contributing to operational improvements such as increased productivity,



improved quality, reduced scrap, production efficiency, reduced downtime or better decision‑making.

Industrial and manufacturing problem understanding: Analyse customer processes, production environments and operational constraints to understand the real problem behind the initial technical request. Work with plant personnel, engineers, process experts and management to identify relevant variables, available data, operational bottlenecks and opportunities for AI-driven improvement.

AI project definition: Translate industrial and manufacturing needs into realistic data‑driven projects, defining the use case, technical scope, required data, methodology, architecture, validation strategy, KPIs and implementation roadmap.

Technical leadership and hands‑on: Provide technical direction and contribute to project developments. Review modelling strategies, experimental approaches, validation methodologies and technical decisions, ensuring both scientific quality and industrial applicability.

Client and partner management: Act as one of the main technical interfaces with industrial customers and project partners. Manage expectations, communicate project progress, explain technical decisions and limitations, facilitate decision‑making and maintain alignment between technical activities and operational objectives.

Technology assessment: Stay up to date with emerging AI technologies and assess their applicability to industrial challenges, including advanced Machine Learning, Deep Learning, time‑series modelling, GNNs, PINNs, Generative AI, LLMs and Agentic AI.

Opportunity development: Collaborate with industrial customers, business development teams and r

📌 Ai Project Leader (Barcelona)
🏢 Eurecat
📍 Barcelona