Machine Learning EngineerHybrid – Cork | Contract | €600–€650 per day (DOE)Role OverviewWe are hiring a Machine Learning Engineer to design, productionise, and scale machine learning solutions that deliver measurable business value. This role focuses on building robust ML systems and infrastructure that move models from experimentation into reliable, secure, and high-performing production environments.You will work at the intersection of data science and software engineering, ensuring that machine learning models are not only accurate, but scalable, maintainable, and operationally resilient.Key ResponsibilitiesDesign, train, and deploy machine learning models to solve business problems across areas such as prediction, classification, optimisation, and automationDevelop end-to-end ML pipelines covering data ingestion, preprocessing, feature engineering, model training, validation, and deploymentImplement and maintain scalable model training workflows, including distributed training and hyperparameter optimisationProductionise models through API development, batch and real-time inference systems, and containerised deployment patternsBuild and manage feature stores and reusable ML components to improve development efficiency and model consistencyMonitor model performance in production, including drift detection, retraining strategies, A/B testing, and performance benchmarkingApply rigorous evaluation techniques to ensure robustness, fairness, interpretability, and business alignmentImplement MLOps best practices, including experiment tracking, model versioning, CI/CD pipelines, automated testing, and reproducibility standardsCollaborate closely with data scientists, data engineers, and software teams to translate business requirements into scalable ML solutionsResearch and assess new machine learning tools, frameworks, and techniques to enhance technical capability and delivery qualityTechnical Skills & ExpertiseStrong programming capability in Python with hands-on experience in frameworks such as TensorFlow, PyTorch, or scikit-learnSolid understanding of supervised and unsupervised learning, deep learning architectures, ensemble methods, and statistical modellingExperience building scalable data and ML infrastructure using AWS, Azure, or GCPPractical knowledge of distributed computing tools such as Spark or Dask for large-scale data processingExperience deploying models using containerisation technologies (e.g., Docker, Kubernetes)Strong understanding of MLOps tooling, including model tracking, deployment automation, and monitoring frameworksExperience implementing CI/CD workflows tailored for machine learning systemsProficiency in SQL and working with relational databases and large datasetsUnderstanding of model explainability, bias detection, and responsible AI practicesStrong problem-solving skills and the ability to work on ambiguous, evolving requirementsClear communication skills with the ability to explain technical concepts to both technical and non-technical stakeholdersExperience & QualificationsBachelor's degree in Computer Science, Data Science, Mathematics, Engineering, or a related technical discipline3+ years of experience building and deploying machine learning models in production environmentsDemonstrated experience developing scalable ML pipelines and infrastructureExperience operating in cloud-based production environmentsProven track record of delivering machine learning solutions that drive business impact