Senior Machine Learning Engineer

Gmassconsulting; Greater London; On-site; GBP 80,000 - GBP 100,000; Full time

Job summary

A leading Audit and Advisory business in Greater London is seeking a Machine Learning Engineer to shape and scale their pricing technology. This role requires building and supporting ML lifecycle tooling, collaborating with data science, actuarial, and product teams. The ideal candidate holds a relevant degree and has strong experience in managing ML models, Python applications, and familiarity with Kubernetes and cloud platforms. Competitive salary and extension opportunities after 12 months are available.

Qualifications

• BSc or MSc in a relevant field.
• Experience managing the full ML model lifecycle.
• Strong Python skills with relevant libraries.

Responsibilities

• Support ML lifecycle tooling for deployment and monitoring.
• Maintain and enhance the Kubeflow environment.
• Create analytics tools to reduce manual processes.

Skills

ML lifecycle management

Python applications

Kubernetes

Statistical methods

Cloud platforms (AWS)

DevOps practices

Education

Bachelor's or Master's degree in Statistics, Data Science, Computer Science

Tools

Kubeflow

Docker

CI/CD

Snowflake

SQL

Job description

Overview

G MASS Consulting are supporting a leading Audit and Advisory business. We're looking for a Machine Learning Engineer to shape and scale their pricing technology. In this role, you'll design and own ML platforms that streamline pricing workflows, support rapid model deployment, and ensure models perform reliably at scale. Partnering with Data Science, Actuarial, and Product teams.

Responsibilities

• Build and support ML lifecycle tooling for model deployment, monitoring, and alerting
• Maintain and improve the Kubeflow environment for Data Scientists and Actuaries
• Create pricing analytics tools to accelerate impact analysis and reduce manual work
• Collaborate with pricing and product teams to deliver high-impact tooling

Qualifications

• Bachelor's or Master's degree in Statistics, Data Science, Computer Science, or a related field
• Strong experience managing the full ML model lifecycle (batch and online)
• Solid understanding of statistical methods, including GLMs and modern ML techniques
• Proven ability to build and deploy production-quality Python applications (pandas, scikit-learn)
• Experience with DevOps and ML tooling, including Kubernetes, Docker, CI/CD, and git-based workflows
• Familiarity with cloud platforms (AWS) and cloud data warehouses (Snowflake/SQL)

Benefits

Salary: to be discussed, depending on experience

Length: 12 months, with the view to extend