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Senior machine learning engineer

Hallaar
Vivid Resourcing
Publiée le Publiée il y a 18 h
Description de l'offre

The RoleAs a Machine Learning Engineer, you will be in charge of model development to deployment and production monitoring. You will be challenged with real-world, large-scale challenges whilst applying solid engineering principles to build robust, scalable, and reliable machine learning systems.

Do you have the right skills and experience for this role Read on to find out, and make your application. Key ResponsibilitiesDesign, build, and deploy machine learning models for production use cases such as recommendation systems, NLP, computer vision, and predictive analyticsDevelop and maintain scalable pipelines for training, evaluation, and inferenceWork with both structured and unstructured data across a variety of domainsImplement efficient data preprocessing, feature engineering, and transformation workflowsEnsure data quality, integrity, and compliance with governance standards (e.g., GDPR)Optimize models for performance, scalability, and cost-efficiency in productionCollaborate closely with data engineers, software engineers, and product stakeholdersDeploy and manage models using cloud platforms (AWS, Azure, or GCP) and containerization toolsBuild monitoring, validation, and testing frameworks to ensure model reliabilityContinuously improve model performance through experimentation and iterationContribute to MLOps practices, including CI/CD, model versioning, and reproducibility Your Profile3–5+ years of experience in Machine Learning Engineering, AI Engineering, or a related fieldStrong programming skills in PythonHands-on experience with ML/DL frameworks such as TensorFlow or PyTorchSolid understanding of machine learning algorithms, evaluation methods, and optimization techniquesProven experience building and deploying ML pipelines in production environmentsFamiliarity with data engineering concepts (ETL/ELT, data pipelines)Experience with cloud platforms xjsrcvq (AWS, Azure, or GCP)Experience with containerization tools (Docker, Kubernetes)Understanding of MLOps tools and practices (e.g., MLflow, Airflow)Experience working with large-scale or complex datasetsAwareness of data privacy and governance best practices What's OfferedCompetitive salary and comprehensive benefits packageHybrid working environmentOpportunity to work on impactful, scalable ML systems in a modern tech settingClear path for professional growth and development ApplyIf this opportunity interests you, apply now or send your CV along with a short cover letter to .

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