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My client is looking for a Machine Learning Engineer to help design, build, and deploy production-grade AI systems that solve complex, real-world problems. This role sits at the core of their AI platform, working closely with data science, product, and engineering teams to take models from experimentation to scale. The Opportunity: You will be responsible for developing, optimising, and deploying machine learning models that power intelligent, enterprise-ready applications. This is a hands-on role for someone who enjoys working across the full ML lifecycle – from data pipelines to model performance in production. Why This Role: This is an opportunity to work on highimpact AI systems where models move quickly from concept to production. My client is building scalable AI platforms, and this role will play a critical part in turning advanced ML into reliable, enterprise-grade solutions.
Job Responsibility:
Design, build, and deploy scalable machine learning models in production environments
Collaborate with data scientists, product managers, and engineers to translate business problems into ML solutions
Build and maintain robust data pipelines and feature stores
Optimise model performance, reliability, and scalability
Implement model monitoring, evaluation, and continuous improvement frameworks
Contribute to MLOps practices, including CI/CD, versioning, and deployment workflows
Stay current with advances in machine learning, AI, and applied research
Requirements:
Proven experience as a Machine Learning Engineer or in a similar applied ML role
Strong foundations in machine learning, statistics, and algorithms
Hands-on experience with Python and ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn)
Experience deploying models in production using cloud platforms and containerised environments
Familiarity with MLOps tools and best practices
Ability to work effectively in cross-functional, fast-paced environments