Machine Learning Engineer
Hybrid – Cork | Contract | €600–€650 per day (DOE)
Role Overview
We 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 Responsibilities
Design, train, and deploy machine learning models to solve business problems across areas such as prediction, classification, optimisation, and automation
Develop end-to-end ML pipelines covering data ingestion, preprocessing, feature engineering, model training, validation, and deployment
Implement and maintain scalable model training workflows, including distributed training and hyperparameter optimisation
Productionise models through API development, batch and real-time inference systems, and containerised deployment patterns
Build and manage feature stores and reusable ML components to improve development efficiency and model consistency
Monitor model performance in production, including drift detection, retraining strategies, A/B testing, and performance benchmarking
Apply rigorous evaluation techniques to ensure robustness, fairness, interpretability, and business alignment
Implement MLOps best practices, including experiment tracking, model versioning, CI/CD pipelines, automated testing, and reproducibility standards
Collaborate closely with data scientists, data engineers, and software teams to translate business requirements into scalable ML solutions
Research and assess new machine learning tools, frameworks, and techniques to enhance technical capability and delivery quality
Technical Skills & Expertise
Strong programming capability in Python with hands-on experience in frameworks such as TensorFlow, PyTorch, or scikit-learn
Solid understanding of supervised and unsupervised learning, deep learning architectures, ensemble methods, and statistical modelling
Experience building scalable data and ML infrastructure using AWS, Azure, or GCP
Practical knowledge of distributed computing tools such as Spark or Dask for large-scale data processing
Experience deploying models using containerisation technologies (e.g., Docker, Kubernetes)
Strong understanding of MLOps tooling, including model tracking, deployment automation, and monitoring frameworks
Experience implementing CI/CD workflows tailored for machine learning systems
Proficiency in SQL and working with relational databases and large datasets
Understanding of model explainability, bias detection, and responsible AI practices
Strong problem-solving skills and the ability to work on ambiguous, evolving requirements
Clear communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders
Experience & Qualifications
Bachelor's degree in Computer Science, Data Science, Mathematics, Engineering, or a related technical discipline
3+ years of experience building and deploying machine learning models in production environments
Demonstrated experience developing scalable ML pipelines and infrastructure
Experience operating in cloud-based production environments
Proven track record of delivering machine learning solutions that drive business impact