Azure AI & Machine Learning Engineer

NTT DATA Europe & Latam

Emilia-Romagna

In loco

EUR 70.000 - 90.000

Tempo pieno

43 ore fa
Candidati tra i primi
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Competenze

  • Bachelor's degree or equivalent practical experience.
  • 5+ years in ML/AI-focused roles.
  • Strong experience deploying AI/ML on Microsoft Azure.
  • Solid understanding of end-to-end ML pipelines and MLOps concepts.
  • Hands-on CI/CD and monitoring for AI systems.
  • Expertise in data preprocessing, feature engineering, and data quality.
  • Knowledge of vector databases, embeddings, and retrieval-based AI systems.
  • Experience evaluating ML frameworks and tooling based on use-case needs.
  • Awareness of security, cost optimization, and performance in cloud-native AI solutions.
  • Excellent verbal and written communication in English.

Mansioni

  • Architect and deploy AI solutions using Microsoft Azure services.
  • Design end-to-end ML architectures from data ingestion to monitoring.
  • Implement MLOps practices, including CI/CD pipelines and monitoring.
  • Preprocess and analyze data for AI and retrieval-based systems.
  • Create and manage vector indexes to support AI search and retrieval use cases.
  • Evaluate, select, and integrate ML frameworks and tools.
  • Ensure AI solutions meet security, cost, and performance standards.

Conoscenze

ML/AI experience
Azure cloud
MLOps
CI/CD pipelines
Data preprocessing
Feature engineering
Vector indexes / embeddings
Retrieval-based AI
Security, cost & performance awareness
English communication

Formazione

Bachelors in Computer Science, Informatics, Engineering, or equivalent

Strumenti

Microsoft Azure
Vector databases
Embeddings
ML frameworks & tooling
CI/CD in production
Production monitoring

Descrizione del lavoro

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.



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

  • Good awareness of security, cost optimization, and performance considerations in cloud-native AI solutions

  • Excellent verbal and written communication skills in English

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