Machine Learning Engineer

Markerstudy Group; Greater Manchester; On-site; GBP 45,000 - 65,000; Full time

Job summary

A leading insurance provider in the UK is seeking a Machine Learning Engineer to automate and optimize their modelling processes. You will collaborate closely with data science teams to build MLOps frameworks within a cloud-based environment, leveraging tools such as Azure ML and Kubernetes. Ideal candidates have a strong background in software engineering and deep understanding of machine learning lifecycle and methodologies.

Qualifications

• Experience in building MLOps / DevOps environments.
• Understanding of machine learning models and processes.
• Proficient in verbal and written communication.

Responsibilities

• Build an MLOps environment for automation of machine learning.
• Create pipelines for model updates and monitoring.
• Collaborate with data science teams for model integration.

Skills

DevOps

MLOps

Azure ML

Kubernetes

Python

Education

Master's degree in a STEM or DS/ML/AI discipline

Tools

Docker

AWS

Google Cloud

Job description

Overview

Markerstudy Group have an exciting opportunity for a machine learning engineer to fill out the automation, pipelining, DevOps, and modelling aspects of Markerstudy's market-leading technical modelling and pricing team. You will productionise novel insurance modelling processes as an automated machine learning pipeline within a cloud-based environment.

Markerstudy is a leading provider of private insurance in the UK, insuring around 5% of the private cars on the UK roads, 20% of commercial vehicles and over 30% of motorcycles in total premium levels of circa £1b. Most of Markerstudy's business is written as the insurance pricing provider behind household names such as Tesco, Sainsbury's, O2, Halifax, AA, Saga and Lloyds Bank to list a few.

As a Machine Learning Engineer, you will help build and maintain the pricing team's MLOps and ML Lifecycle environment to support the creation of pipelines by automating the sophisticated machine learning models and processes that underpin our market-leading technical modelling and pricing function.

Key Responsibilities

• Build an MLOps / DevOps environment to support machine learning automation
• Build the pipelines that automate the regular model update and monitoring processes
• Build a framework that supports the creation, deployment, maintenance, and monitoring elements that non-data scientist and machine learning analysts produce, including assisting with hyper-parameter tuning, feature engineering, feature selection, and validation, reporting and visualisation, and communication processes.
• Work closely with the data science team to integrate modelling approaches and techniques

Key Skills and Experience

• Previous experience as a DevOps / MLOps engineer
• Experience in Azure ML or databricks, or similar industry approved technology stack (i.e. AWS, Kubernetes and Docker, Google Cloud)
• Understanding of machine learning models and the modelling process, from data ingestion and cleaning to deployment and modelling - from the ground-up, not only through the use of packages and libraries
• Proficient at communicating results in a concise manner both verbally and written
• Previous industry experience in a STEM role or educated to the Master\'s level in a STEM or DS / ML / AI or maths-based discipline.

Behaviours

• Collaborative and team player
• Logical thinker with a professional and positive attitude
• Passion to innovate and improve processes
• Strong grasp of industry standards, and proficient in either Python, R, or both