Machine Learning Engineer

A Machine Learning Engineer designs, develops, and implements machine learning models that automate and enhance processes in financial services, such as risk analysis, fraud detection, and trading algorithms. They work with vast datasets to build predictive models and optimize decision-making systems, ensuring the models are accurate, efficient, and scalable for real-world applications.

Key Responsibilities

• Develop and deploy machine learning models for applications like fraud detection, credit scoring, and automated trading.
• Work with data scientists to collect, clean, and preprocess data for model training. Collaborate with software engineers and IT teams to integrate models into production systems.
• Implement and optimise algorithms to ensure models run efficiently at scale. Continuously monitor model performance and retrain as needed to ensure accuracy and reliability.

Important Skills and Competencies

• Machine learning and AI: Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Strong programming skills in languages such as Python, Java, or C++.
• Data analytics: Experience with data engineering and large-scale data processing tools (e.g., Hadoop, Spark). Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) for model deployment.
• Problem solving: Problem-solving skills and the ability to optimise complex algorithms.
• Continuous learning: Keep up with the latest advancements in machine learning, including deep learning and AI.