Machine Learning Specialist

About the role

Operationalise machine learning models and AI capabilities into reliable, scalable, and secure production systems that power paid risk intelligence products.

Key responsibilities

• Design and build scalable model training, deployment, and inference pipelines.
• Deploy models into production using CI/CD, containerisation, and cloud infrastructure while meeting performance, latency, and reliability targets.
• Implement model versioning, monitoring, and automated retraining; detect and respond to model and data drift.
• Establish operational standards for AI systems and ensure compliance with privacy, security, and regulatory requirements.
• Partner with data scientists to productionise models and work with product and engineering teams to integrate ML into customer‑facing products.
• Support explainability, auditability, and responsible AI practices, documenting models and operational decisions clearly.

Required profile

• 8+ years of experience in machine‑learning engineering, MLOps, or software engineering.
• Strong software engineering skills in Python and cloud environments.
• Hands‑on experience with ML platforms such as MLflow, Kubeflow, SageMaker and with Kubernetes.
• Bachelor’s degree in Computer Science, Engineering, Information Systems or related field; advanced degree in Big Data, AI or similar is a plus.

Required skills

• Python
• CI/CD pipelines
• Containerisation (Docker, etc.)
• Cloud platforms (AWS, GCP, Azure)
• MLflow
• Kubeflow
• SageMaker
• Kubernetes