Machine Learning Specialist

Main mission

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

Key responsibilities

• ML Systems & Deployment
• Design and build scalable model training, deployment, and inference pipelines
• Deploy models into production using CI/CD, containerisation, and cloud infrastructure
• Ensure models meet performance, latency, and reliability requirements MLOps & Lifecycle Management
• Implement model versioning, monitoring, and automated retraining mechanisms
• Detect and respond to model drift, data drift, and performance degradation
• Establish operational standards for AI systems Collaboration with Data Science & Product
• Partner with Data — Scientists to productionise and scale models
• Work with Product and Engineering teams to integrate ML into customer‑facing products
• Translate experimental work into robust production assets Governance, Security & Ethics
• Ensure ML systems comply with privacy, security, and regulatory requirements
• Support explainability, auditability, and responsible AI practices
• Document models and operational decisions clearly Job Profile – Required Skills And Knowledge
• 8+ years experience in ML engineering, MLOps, or software engineering roles
• Strong software engineering skills in Python and cloud environments
• Hands‑on experience with ML platforms (MLflow, Kubeflow, SageMaker) and Kubernetes Required Qualifications
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field
• MS in Big Data, AI or relevant fields