R&T Engineer
Pubblicato il 24-08-2026 - Consulting / Consulenza / Coaching in Italia
About the Company A group of innovation-based industrial and packaging solutions companies operating globally, headquartered in Bologna, Italy.
Coesia's companies are leaders in automation solutions for multiple industries, ranging from automated processing and packaging machinery, testing and vision systems, to digital printing technologies, factory flow solutions and high-performance transmissions.
Our customers include leading players in a wide range of sectors, including aerospace, automotive, consumer and luxury goods, ceramics, electronics and semiconductors, pharma and healthcare, energy storage systems, tobacco and next-generation products.
Coesia operates in 34 countries with 21 different companies and employs over 8,000 people as of 2026.
About the Role Context The AI technology and related applications are evolving at an unprecedented pace. To stay ahead, we need to continuously monitor the technology landscape, identify the most promising developments for our business needs, and deliver prototypes, minimum viable products,
or proofs of concept to test these technologies.Our business needs include enhancing the effectiveness of engineering processes and integrating AI technology into our products and services.
Position The primary responsibility will be to deliver 3-4 validated PoCs per year, with at least one PoC graduating to a production roadmap, so Coesia stays ahead in applying AI to industrial engineering.
Daily Tasks Scout & evaluate: track research papers, benchmarks, OSS repos, webinars; curate a monthly "AI radar" for stakeholders
Prototype & validate: design and code PoCs/MVPs; measure business impact
Collaborate: work closely with cross-functional teams, including designers, data scientists, marketing and IT to co-create technology workflows and innovation roadmaps
MLOps & software engineering: containerise workloads, automate CI/CD, and instrument model monitoring
Data wrangling: acquire, clean, and label data; fine-tune or distil large model