Senior Machine Learning Engineer

About the Company: We are partnering with a leading organisation in the Financial Services and Insurance sector, focused on driving AI innovation to support growth and scalability. As part of their AI transformation, we are seeking Senior Machine Learning Engineers to help architect and implement a scalable ML platform on Google Cloud Platform (GCP) and migrate existing models from Dataiku into the GCP ecosystem. Role Overview: As a Senior Machine Learning Engineer, you will work within an agile squad alongside data scientists, data engineers, software developers, and DevOps teams. Your primary focus will be on building and extending the ML platform, deploying AI models into production, and ensuring robust model monitoring and governance. Key Responsibilities: Architect, design, and implement an ML platform on GCP to support scalable AI solutions. Migrate existing machine learning models from Dataiku (or similar platforms) into GCP. Develop end-to-end ML pipelines using GCP services such as Vertex AI, AI Platform, and BigQuery. Deploy AI models (both GenAI and non-GenAI) into production, ensuring scalability and security. Implement model monitoring frameworks to track data drift, concept drift, and overall performance. Establish and enforce standardised guardrails for model deployment, ensuring robust security and compliance. Collaborate with cross-functional teams in an agile environment, contributing to continuous improvement. Skills & Experience Required: 5 years of experience as a Machine Learning Engineer or similar role. Strong expertise in Google Cloud Platform, particularly Vertex AI, AI Platform, and BigQuery. Proven experience building and deploying ML platforms and AI models in production environments. Hands-on experience migrating models from Dataiku or similar tools into cloud platforms. Familiarity with GenAI models and open-source AI frameworks. Solid programming skills in Python, with experience in machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. Understanding of ML model monitoring, including data drift and concept drift detection. Knowledge of containerisation technologies (Docker) and orchestration tools (Kubernetes). Experience in the Financial Services or Insurance industry is highly desirable.