Machine Learning Engineer

Diagonal recruitment; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

Diagonal Recruitment seeks a skilled machine learning engineer to develop, train and optimise models for production use. You will evaluate performance, build pipelines and support deployment with monitoring, collaborating across engineering, product and data teams.

The ideal candidate balances experimentation with operational needs, is data-driven and able to explain complex concepts to non-technical stakeholders.

Qualifications

• Experience deploying models into production environments.
• Comfortable balancing experimentation with operational requirements.
• Data-driven and analytical.
• Able to explain technical concepts to non-technical stakeholders.

Responsibilities

• Develop, train and optimise machine learning models
• Evaluate model performance and improve accuracy
• Build reusable machine learning pipelines
• Support deployment and monitoring of models in production
• Collaborate with engineering, product and data teams
• Contribute to model governance and lifecycle management

Skills

Python

Scikit-learn

PyTorch

TensorFlow

MLflow

SQL

Git

AWS

Azure

GCP

Tools

MLflow

Git

Job description

We work with organisations looking to build, optimise and operationalise machine learning capabilities that create measurable business value. The focus is on production deployment, performance and maintainability rather than experimentation alone.

Role Overview

• Develop, train and optimise machine learning models
• Evaluate model performance and improve accuracy
• Build reusable machine learning pipelines
• Support deployment and monitoring of models in production
• Collaborate with engineering, product and data teams
• Contribute to model governance and lifecycle management

Tools & Technologies (required0

• Python
• Scikit-learn
• PyTorch and/or TensorFlow
• MLflow or equivalent
• SQL
• Git
• Cloud environments including AWS, Azure or GCP

Highly preferred

• Experience with foundation models
• Exposure to reinforcement learning
• Knowledge of AI evaluation frameworks

About You

• Comfortable balancing experimentation with operational requirements
• Data-driven and analytical
• Curious but pragmatic
• Able to explain technical concepts to non-technical stakeholders

Additional Information & Benefits

• Permanent, contract and advisory engagements
• Ideal for practitioners who have deployed models into production environments