Azure AI & Machine Learning Engineer

Who We AreWe 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 AreWe 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 DoingArchitect and deploy AI solutions using Microsoft Azure servicesDesign end-to-end AI/ML architectures covering data ingestion, feature engineering, model training, deployment, and monitoringImplement MLOps practices, including CI/CD pipelines and operational monitoringPreprocess and analyze data to ensure high-quality inputs for AI and retrieval-based systemsCreate and manage vector indexes to support AI search and retrieval use casesEvaluate, select, and integrate appropriate ML frameworks, tools, and platformsEnsure AI solutions comply with security, cost, and performance standardsWhat You Bring AlongBachelor’s degree in Computer Science, Informatics, Engineering, or equivalent practical experienceMinimum 5+ years of experience ML and AI-focused rolesStrong experience designing and deploying AI/ML solutions on Microsoft AzureSolid understanding of end-to-end ML pipelines and MLOps conceptsHands-on experience with CI/CD pipelines and production monitoring for AI systemsExpertise in data preprocessing, feature engineering, and data quality assurancePractical knowledge of vector databases, embeddings, and retrieval-based AI systemsExperience evaluating and selecting ML frameworks and tooling based on use case needsGood awareness of security, cost optimization, and performance considerations in cloud-native AI solutionsExcellent verbal and written communication skills in English

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