Azure AI & Machine Learning Engineer
NTT DATA Romania ·
- Category
- ML ai
NTT DATA Romania is hiring an Azure AI & Machine Learning Engineer in Cluj to architect and deploy AI/ML solutions on Microsoft Azure: designing end-to-end pipelines, implementing MLOps with CI/CD, and building vector-based retrieval systems for enterprise clients across banking, telecom, 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