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We are currently partnering with several leading technology consultancies and data-driven enterprises who are looking for experienced Machine Learning Engineers to join their high-performing teams. These organisations are at the forefront of innovation, offering the chance to work on complex, real-world business challenges where your expertise in production-grade ML systems will have a tangible impact.
Job Responsibility
Bridge the gap between data science and software engineering, taking ownership of the end-to-end ML lifecycle from initial architecture and data pipeline construction to model training, deployment, and ongoing monitoring
define technical strategy, implement robust MLOps practices, and optimize infrastructure to ensure scalable and reliable solutions
mentor junior engineers and collaborate closely with cross-functional stakeholders
Requirements
Extensive experience building and deploying machine learning models in production environments
proficiency in Python and major ML frameworks such as PyTorch, TensorFlow, or Scikit-learn
strong understanding of MLOps, CI/CD pipelines, and containerisation technologies (Docker, Kubernetes)
hands-on experience with cloud platforms (AWS, GCP, or Azure) and scalable infrastructure design
ability to design and maintain robust data pipelines (ETL/ELT) and feature stores
proven track record in technical leadership, mentoring, and guiding engineering teams
deep knowledge of ML system design, software architecture, and performance optimization
strong communication skills with experience in stakeholder management and cross-functional collaboration