AI Azure ML Engineer
Pubblicato il 11-09-2026 - Ntt Data Europe & Latam in Bologna
Who We Are
We operate beyond organizational silos, combining deep expertise in data engineering and AI-driven solutions to design and deliver scalable, user-centric applications and modern data architectures. We support a wide range of industries, including Automotive, Banking, Insurance, Telecommunications, E-commerce, and Public Services. Our focus is on building robust, enterprise-grade data platforms and enabling advanced analytics and AI capabilities within the Microsoft ecosystem, including Azure, Microsoft Fabric, and the Power Platform. We specialize in designing secure, high-performance solutions that integrate data pipelines, AI models, and Generative AI use cases to deliver real business value.
What You’ll Be Doing
Architect and deploy AI solutions using Microsoft Azure services.
Design end-to-end AI/ML architectures covering data ingestion, feature engineering, model training, deployment, and monitoring.
Implement MLOps practices, including CI/CD pipelines and operational monitoring.
Preprocess and analyze data to ensure high-quality inputs for AI and retrieval-based systems.
Create and manage vector indexes to support AI search and retrieval use cases.
Evaluate, select, and integrate appropriate ML frameworks, tools, and platforms.
Ensure AI solutions comply with security, cost, and performance standards.
What You Bring Along
Bachelor’s degree in Computer Science, Informatics, Engineering, or equivalent practical experience
Minimum 5+ years of experience ML and AI-focused roles
Strong experience designing and deploying AI/ML solutions on Microsoft Azure.
Solid understanding of end-to-end ML pipelines and MLOps concepts.
Hands-on experience with CI/CD pipelines and production monitoring for AI systems.
Expertise in data preprocessing, feature engineering, and data quality assurance.
Practical knowledge of vector databases, embeddings, and retrieval-based AI systems.
Experience evaluating and selecting ML frameworks and tooling based on use case needs.
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