Senior Machine Learning Engineer
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
- 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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