Machine Learning Engineer

As a Machine Learning Engineer, you will:
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• Design, build, and deploy scalable machine learning models into production SaaS environments.
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• Develop and optimise data pipelines for training, testing, and monitoring ML models.
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• Collaborate with product managers, data scientists, and engineers to deliver AI -driven solutions.
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• Ensure that all models are explainable, ethical, and aligned with core business goals.
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• Contribute to MLOps best practices, improving model lifecycle management and deployment automation.
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• Stay current with emerging trends in machine learning, generative AI, and data engineering.
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  Key Responsibilities:<\/span><\/div>

  • Build and maintain ML models supporting predictive analytics, recommendation engines, and automation features.
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  • Integrate ML solutions into scalable APIs and microservices architectures.
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  • Monitor and fine -tune model performance for real -world production systems.
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  • Work with large, complex datasets to ensure model accuracy and reliability.
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  • Produce technical documentation and share knowledge across the team.
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    Requirements<\/h3>

    Key Skills & Experience:<\/div>

    • Proven experience as a Machine Learning Engineer<\/b> or similar role in a production environment.
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    • Strong programming proficiency in Python<\/b>, using ML frameworks such as TensorFlow, PyTorch, or scikit -learn.
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    • Experience with cloud computing platforms (AWS, Azure, or GCP).
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    • Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, or SageMaker).
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    • Strong database knowledge (SQL and NoSQL).
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    • Sound understanding of algorithms, data structures, and model optimisation.
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      Qualifications:<\/div>

      • Bachelor’s or Master’s degree<\/b> in Computer Science, Data Science, Mathematics, or a related field (or equivalent industry experience).
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      • Proven experience delivering ML models into production.
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      • Desirable: cloud or data certifications (AWS, GCP, or Azure).
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        Benefits<\/h3>

        Competitive salary and potential equity options.
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        Hybrid working from their central London office.
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        Opportunity to develop AI -powered features for an innovative, fast -scaling SaaS product.
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        Continuous learning culture with dedicated training budgets and mentorship.
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        Collaborative, forward -thinking, and diverse company culture.
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