ML Engineer - Production Pipelines & Real-Time Deployment

Intact Insurance UK; Greater London; On-site; GBP 65,000 - 100,000; Full time

Job summary

Intact Insurance UK is seeking a Machine Learning Engineer to design, deploy and operate production-grade ML systems in London or Horsham. You’ll partner with data scientists and platform teams to build scalable pipelines, convert notebooks to robust code, and shape the ML roadmap with a focus on reliable, observable deployments.

You’ll apply Python, PySpark, Databricks and MLflow across batch and real-time use cases, with strong emphasis on testing, documentation and operational resilience.

Qualifications

• Experience building production ML systems and pipelines.
• Strong Python, SQL, and PySpark skills.
• Familiarity with CI/CD and cloud deployment patterns.

Responsibilities

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

Skills

Python

PySpark

SQL

Production code

Communication

Tools

Databricks

MLflow

Delta Lake

CI/CD

Containerisation

APIs

Job description

Intact Insurance UK is seeking a Machine Learning Engineer to design, deploy and operate production-grade ML systems in London or Horsham. You’ll partner with data scientists and platform teams to build scalable pipelines, convert notebooks to robust code, and shape the ML roadmap with a focus on reliable, observable deployments.

You’ll apply Python, PySpark, Databricks and MLflow across batch and real-time use cases, with strong emphasis on testing, documentation and operational resilience.