Working at Sky Italy in Data Platform & Analytics (DP&A) is a unique opportunity to shape the best solutions to unlock the full value of data by adopting advanced analytics and derive knowledge to gain insights on how to keep our customers experience at top level.
Role overview
As a Senior Machine Learning Engineer, you will play a strategic and hands-on role in designing, building, and scaling Machine Learning solutions within the DP&A area.
You will contribute the evolution of advanced analytics across our Data Platform by championing modern ML approaches, strengthening end-to-end ML infrastructure, and accelerating the adoption of Big Data and GenAI capabilities.
This role blends deep technical expertise with leadership influence and you will help shape the ML vision, ensure scalability, and deliver measurable business impact through production-ready ML/AI initiatives.
Key Responsibilities
1. Contribute to the execution of the AI/ML strategy aligned with business priorities, identifying and prioritizing high-impact use cases and translating them into scalable, production-ready solutions
2. Lead the technical roadmap and evolution of the end-to-end ML platform, ensuring robust architecture, strong MLOps practices, and full lifecycle governance (development, deployment, monitoring, retraining, compliance)
3. Drive the adoption, experimentation, and industrialization of ML solutions across relevant business domains
4. Ensure production systems meet enterprise standards for scalability, reliability, security, and regulatory compliance, while continuously monitoring model performance and business KPIs
5. Contribute to the growth of ML engineering capabilities by mentoring junior team members and collaborating effectively with senior technical and business stakeholders to foster innovation and engineering excellence
Skills & Requirements
6. Proven experience designing, deploying, and scaling ML systems in production environments.
7. Strong expertise in ML frameworks, distributed systems, data modeling, and cloud-native software architecture
8. Solid hands-on experience with MLOps practices, CI/CD pipelines, infrastructure automation, monitoring, and ML lifecycle management
9. Experience working with large-scale structured and unstructured datasets within Big Data ecosystems
10. Advanced proficiency in Python and core ML/data science libraries (, NumPy, Pandas, Scikit-learn, TensorFlow)
11. Strong foundation in mathematics and statistics, combined with strategic thinking, stakeholder engagement, and leadership capabilities
Experience & Education
12. 5+ years of experience in Machine Learning / AI / Data Science, with proven hands-on production experience
13. Master’s degree in Computer Science, Engineering, Mathematics, or a related quantitative field
14. Professional proficiency in English