Machine Learning Engineer

Overview

As a Machine Learning Engineer at Intact Insurance, you will help design and operate production-grade ML systems that power business decisions. You’ll collaborate with Data Scientists and engineering teams to shape the ML roadmap and deliver scalable solutions. You’ll productionise models, manage deployment across batch and real-time environments, and implement monitoring and automated retraining. This role sits at the intersection of data, platform, and business, offering impact across the organization and opportunities to grow your career. You’ll join a mission-driven team focused on making insurance simpler, faster, and more responsive.

Pay / Benefits

• annual discretionary bonus
• Up to 11% pension contributions
• Hybrid working + flexible hours
• 25 days annual leave + bank holidays + buy/sell options
• Health & wellbeing + virtual GP
• Career development and mentoring(dedicated)??

Responsibilities

• Build and automate ML pipelines for feature engineering, training, and scoring using Python, PySpark, Databricks, and MLflow
• Productionise Data Science models by turning notebooks into modular, production-ready code
• Deploy models in batch and real-time environments with versioning, promotion, rollback, and scheduled workflows via MLflow and APIs
• Implement monitoring and observability, including drift detection, performance alerts, logging, and automated retraining
• Collaborate with Data Engineering and Platform teams on CI/CD integration, pipeline performance, compute optimization, and secure deployment patterns
• Maintain engineering standards with high-quality testing, documentation, reproducibility, and operational reliability

Key requirements

• Experience with ML platforms including Databricks, MLflow, Delta Lake, and cloud environments
• Proficient in Python, PySpark, and SQL, following production coding best practices
• Understanding of data, distributed ML pipelines, and model deployment patterns including monitoring and lifecycle operations
• Exposure to CI/CD, containerisation, and API integration for scalable, production-ready ML systems
• Ability to work technically while communicating effectively with Data Scientists, stakeholders, and cross-functional teams

• strong collaboration
• effective communication
• problem-solving mindset
• Python
• PySpark
• SQL

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