✨ AI Summary
Virtusa Middle East FZ-LLC is seeking a Machine Learning Engineer in Dubai, UAE. The role involves designing and developing scalable machine learning models and AI-driven solutions. Key responsibilities include working with large datasets, training and deploying ML models, building end-to-end ML pipelines, automating workflows, and collaborating with cross-functional teams to integrate models into applications. The ideal candidate will have 7-10 years of experience and a Master of Technology/Engineering in Computers, with a strong background in AI systems development and MLOps.
Designs and develops scalable machine learning models and AI-drivensolutions to address complex business challenges and enhance decision-making processesKEY RESPONSIBILITIESWork with large and complex data sets to solve challenging businessproblemsEfficient training and deployment of standard ML, NN, and Agentic models.Develop, train, and optimize machine learning models using state-of-the-artalgorithms and frameworksBuild production grade end-to-end ML pipelines, including data ingestion,transf
Requirements
(3-7) years of experience in building production grade, scalable AI systems.
vision, or generative AI (e.g., LLMs).
Strong ML system architecture skills
Understanding of model serving, API development (FastAPI, Flask), and
optimizing model performance for real-time or batch inference.
MLflow/Kubeflow for model lifecycle management (MLOps)
Comfortable with deploying models on AWS or Azure
Minimum Educational qualifications: Bachelor’s degree in Computer Science,
Engineering, or related field required
Classified: Internal\ FAB
Internal
related field.
Work Experience
7-10Years
Description
Designs and develops scalable machine learning models and AI-drivensolutions to address complex business challenges and enhance decision-making processesKEY RESPONSIBILITIESWork with large and complex data sets to solve challenging businessproblemsEfficient training and deployment of standard ML, NN, and Agentic models.Develop, train, and optimize machine learning models using state-of-the-artalgorithms and frameworksBuild production grade end-to-end ML pipelines, including data ingestion,transformation, model training, validation, and deploymentAutomate workflows for model training, testing, and deployment usingCI/CD pipelines and MLOps toolsCollaborate with cross-functional teams to integrate models into applicationsand deliver end-to-end solutionsFinetune SLMs/LLMs and build complex AI architectures