Senior Machine Learning Engineer

I’m working with a highly established UK property data and analytics business that provides valuation, risk and decisioning technology to some of the country’s largest mortgage lenders and financial services organisations.

They are now looking for a Senior Machine Learning Engineer to take ownership of moving machine learning models from proof of concept into reliable, scalable production systems.

This is a particularly important hire for the business. You will be their dedicated Machine Learning Engineer, working closely with an experienced Data Science team that focuses on model exploration and development, while you lead on the engineering required to deploy, serve, monitor and scale those models in production.

The immediate roadmap includes two major projects.

The first involves processing property imagery and text at significant scale, potentially across hundreds of millions of images, to extract structured property information and generate surveyor-style insights.

The second is a real-time LLM-powered reporting product, where latency, uptime, model performance and cost-efficient serving will all be critical.

Alongside these newer generative AI projects, you will also take responsibility for an established estate of traditional machine learning models already running in production.

This is not a Data Scientist role. The business is looking for someone with strong software and platform engineering fundamentals who understands the complete machine learning lifecycle.

You will be responsible for:

• Turning Data Science prototypes into robust production applications
• Designing batch and real-time inference pipelines
• Deploying and serving machine learning and LLM-based models
• Building automated testing, retraining and deployment workflows
• Monitoring model performance, reliability and infrastructure
• Managing cloud and inference costs at scale
• Improving MLOps, CI/CD and engineering standards
• Working closely with Product, Data Science and Software Engineering teams

The strongest candidates are likely to have:

• Strong commercial Python engineering experience
• Experience deploying machine learning models into production
• Good knowledge of MLOps, model serving and monitoring
• Experience with AWS or another major cloud platform
• Docker and containerised application experience
• Infrastructure as code experience, ideally Terraform or CloudFormation
• Experience with SQL, PySpark, Databricks or AWS Glue
• Exposure to Scikit-learn, PyTorch, LightGBM or similar libraries
• An understanding of LLMs, multimodal models or generative AI applications

Deep commercial LLM experience is not essential.

The hiring manager is more interested in strong Machine Learning Engineers who understand production systems, infrastructure and engineering fundamentals. Candidates who have built traditional ML applications and have been actively developing their knowledge of LLMs will be strongly considered.

They are looking for someone who can operate independently, take ownership of broad problems and make sensible technical and commercial decisions without needing close supervision.

Relevant backgrounds could include insurance, property technology, financial services, computer vision, document intelligence or medical imaging.

Package and working pattern

• £100,000 base salary, with flexibility towards £110,000 for an exceptional candidate
• Discretionary bonus of up to 10%
• 7.5% employer pension contribution
• Private medical insurance
• 25 days’ holiday plus additional wellbeing and personal days
• Three days per week in the London office
• Monday and Tuesday are fixed team days, with flexibility over the third day

This would suit a Senior Machine Learning Engineer who wants genuine ownership of production ML, exposure to both established models and modern generative AI, and the opportunity to solve engineering problems at substantial real-world scale.

Please apply or message me directly for further information.

Burns Sheehan Ltd will consider applications based only on skills and ability and will not discriminate on any grounds.

• Strong commercial Python engineering experience
• Experience deploying machine learning models into production
• Good knowledge of MLOps, model serving and monitoring
• Experience with AWS or another major cloud platform
• Docker and containerised application experience
• Infrastructure as code experience, ideally Terraform or CloudFormation
• Experience with SQL, PySpark, Databricks or AWS Glue
• Exposure to Scikit-learn, PyTorch, LightGBM or similar libraries
• An understanding of LLMs, multimodal models or generative AI applications

Deep commercial LLM experience is not essential.

The hiring manager is more interested in strong Machine Learning Engineers who understand production systems, infrastructure and engineering fundamentals. Candidates who have built traditional ML applications and have been actively developing their knowledge of LLMs will be strongly considered.

They are looking for someone who can operate independently, take ownership of broad problems and make sensible technical and commercial decisions without needing close supervision.

Relevant backgrounds could include insurance, property technology, financial services, computer vision, document intelligence or medical imaging.

• £100,000 base salary, with flexibility towards £110,000 for an exceptional candidate
• Discretionary bonus of up to 10%
• 7.5% employer pension contribution
• Private medical insurance
• 25 days’ holiday plus additional wellbeing and personal days
• Three days per week in the London office
• Monday and Tuesday are fixed team days, with flexibility over the third day

This would suit a Senior Machine Learning Engineer who wants genuine ownership of production ML, exposure to both established models and modern generative AI, and the opportunity to solve engineering problems at substantial real-world scale.