Machine Learning Engineer
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.
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.
Key Responsibilities- 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.
- 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, Tensor Flow, 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.
This is an opportunity to work on high‑impact 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.
Only successful candidates will be contacted.
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