Responsibilities
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- Act as Technical Product Owner (TPO) for AI Data Engineering products, capabilities, and platforms.

- Partner with business stakeholders, AI teams, and architects to translate business requirements into scalable data and AI engineering solutions.

- Define and prioritize platform backlogs, technical roadmaps, and delivery plans.

- Drive adoption of reusable data products, AI services, and platform capabilities across multiple business domains.

- Design, develop, and maintain scalable data pipelines supporting AI, Analytics, Machine Learning, and Generative AI use cases.

- Lead implementation of batch, streaming, and real-time data integration capabilities.

- Build trusted and governed data assets supporting enterprise AI use cases.

- Drive engineering standards for data quality, observability, lineage, monitoring, and reliability.

- Ensure data platforms are secure, scalable, resilient, and compliant with enterprise standards.

- Enable AI solution delivery through feature stores, vector databases, model deployment pipelines, and data services.

- Support implementation of Generative AI, LLM, RAG, and Agentic AI architectures through scalable data foundations.

- Collaborate with AI Engineers and Data Scientists to operationalize AI solutions.

- Establish and maintain MLOps and DataOps practices.

- Design and operate cloud-native AI and Data Platforms.

- Define architecture patterns for data ingestion, transformation, storage, governance, and consumption.

- Optimize platform performance, scalability, reliability, and cost efficiency.

- Lead implementation of Infrastructure-as-Code, CI/CD, monitoring, and observability frameworks.





- Lead Agile squads delivering AI Data Engineering and platform capabilities.

- Facilitate sprint planning, backlog refinement, technical reviews, and delivery governance.

- Promote DevOps, DataOps, and Agile engineering best practices.

- Act as the bridge between business stakeholders, AI teams, platform teams, architects, and delivery organizations.

- Ensure compliance with enterprise security, privacy, data governance, and responsible AI requirements.

- Define and monitor OKRs/KPIs related to platform adoption, data quality, delivery velocity, operational performance, and business value realization.

Requirements

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- Bachelor's or Master's degree in Computer Science, Engineering, Data Engineering, Information Systems, Artificial Intelligence, or related disciplines.

- 5–7+ years of experience in Data Engineering, AI Engineering, Platform Engineering, or Cloud Data Platform roles.

- Proven experience designing enterprise-scale data pipelines and cloud-native data platforms.

- Experience acting as Technical Product Owner, Delivery Lead, Lead Engineer, or Squad Lead.

- Strong expertise in ETL/ELT, Data Lakes, Lakehouse architectures, Data Warehousing, Metadata Management, and Data Governance.

- Hands‑on experience with Azure, AWS, or GCP.

- Understanding of Generative AI, LLMs, Vector Databases, RAG, and AI agent architectures.

- Experience implementing MLOps, CI/CD, Infrastructure-as-Code, and DataOps practices.

- Strong SQL and Python skills.

- Experience working in Agile and Scrum environments.

Core Competencies

Demonstrates expertise in designing and implementing scalable data pipelines and cloud-native data platforms, with a strong focus on AI and Data Engineering solutions. xysqume Proficient in MLOps, DataOps, and Agile methodologies to drive platform adoption and ensure data quality and compliance.

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📌 Lead Data AI Engineer (Milano)
🏢 Jobtailor
📍 Milano