Machine Learning Engineer

We are seeking an innovative Machine Learning Engineer to design, develop, and deploy ML models that solve complex business problems. The ideal candidate will bridge the gap between data science and production engineering, building robust ML systems that deliver value at scale. You will collaborate with data scientists, engineers, and product teams to operationalise machine learning solutions and drive AI innovation., * Design, develop, and deploy machine learning models for production environments, ensuring reliability and scalability.

• Implement MLOps practices including model versioning, automated training pipelines, and continuous model monitoring.
• Build and optimise data pipelines for feature engineering, model training, and real-time inference.
• Collaborate with data scientists to translate research prototypes into production-ready ML systems.
• Design and implement A/B testing frameworks to evaluate model performance and business impact.
• Optimise model performance, latency, and resource utilisation for cost-effective production deployment.
• Build monitoring and alerting systems to track model drift, data quality issues, and prediction accuracy.
• Implement best practices for model governance, documentation, and reproducibility.
• Research and evaluate emerging ML techniques, frameworks, and tools to drive continuous innovation.
• Partner with engineering teams to integrate ML models into applications and services seamlessly.
• Mentor team members on ML engineering best practices and MLOps methodologies.

Do you have experience in SQL?, Do you have a Master's degree?, * 4-6 years of experience in machine learning engineering or applied ML roles in production environments.

• Strong programming skills in Python with experience in ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Proven track record deploying and maintaining ML models in production with high availability requirements.
• Experience with MLOps tools and practices (MLflow, Kubeflow, SageMaker, or Azure ML).
• Solid understanding of machine learning algorithms including supervised, unsupervised, and reinforcement learning.
• Proficiency with data processing frameworks (Spark, Pandas) and building scalable data pipelines.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerisation (Docker, Kubernetes).
• Strong knowledge of model serving technologies (TensorFlow Serving, TorchServe, Seldon, or similar).
• Understanding of software engineering best practices including testing, CI/CD, and version control.
• Experience with distributed computing and optimising code for performance and scalability.
• Excellent problem-solving skills with ability to debug complex ML systems and data issues.
• Master's degree or PhD in Computer Science, Machine Learning, Statistics, or related field is advantageous.