Senior Machine Learning Engineer

London (Hybrid)

This is an opportunity to join a newly established Machine Learning Engineering team building a greenfield AI product within a highly successful global technology business. You'll have significant ownership from day one, helping shape the architecture, ML systems, and product direction of a platform designed to solve complex, high-value challenges at scale.

The Company

They are a global technology organisation operating across multiple international markets, processing billions of customer interactions and transactions each year. Backed by strong executive sponsorship and substantial investment, they are developing a new AI-powered platform focused on operational optimisation and decision intelligence.

The Role

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and operating production-grade ML systems from the ground up.

Responsibilities include:

• Building and owning end-to-end machine learning solutions, from data ingestion through to deployment and monitoring
• Developing scalable data pipelines that process large volumes of real-time and batch data
• Training, deploying, and maintaining machine learning models in production environments
• Working with Databricks, AWS, Python, Kafka, and modern MLOps tooling
• Leveraging machine learning, LLMs, and multimodal AI techniques across structured and unstructured data sources
• Collaborating with engineering, product, and business stakeholders to deliver impactful solutions
• Contributing to system architecture, platform design, and technical best practices
• Driving measurable improvements through AI-powered decisioning and automation

Your Skills & Experience

• Strong commercial experience as a Machine Learning Engineer, MLOps Engineer, or in a similar applied ML role
• Proven experience building and deploying machine learning systems into production
• Experience designing and maintaining scalable data pipelines and ML infrastructure
• Strong Python engineering skills
• Experience with Databricks and cloud platforms such as AWS
• Knowledge of production monitoring, model performance management, and MLOps best practices
• Ability to operate autonomously and take ownership of projects from concept through delivery
• Experience working with large-scale data environments
• Exposure to fraud, payments, financial services, or risk-related machine learning use cases would be advantageous