Senior Machine Learning Engineer

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 ...

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