Overview
The Senior Data & ML Engineer will take on a hands-on engineering role with a rapidly growing financial services business. In this position, the contractor will collaborate closely with Data Scientists to design, build, and scale machine learning solutions, while also focusing on improving the end-to-end machine learning lifecycle. The role emphasizes strong technical skills in data and machine learning engineering, as well as a commitment to establishing best practices for production systems in a hybrid working environment.
Responsibilities
- Design and build scalable data and feature engineering pipelines to support machine learning workloads.
- Work closely with Data Scientists to operationalise models and improve the end-to-end machine learning lifecycle.
- Develop and optimise PySpark-based data processing workflows and training pipelines.
- Build and maintain workflow orchestration frameworks using tools such as Airflow or Databricks Workflows.
- Support model training, deployment, monitoring, and experiment tracking in production environments.
- Contribute to feature engineering and model performance optimisation initiatives.
- Help establish engineering standards, platform capabilities, and best practices across ML and data workflows.
- Collaborate with engineering and product teams to deliver reliable, production-grade machine learning systems.
Requirements
- Strong software, data, or machine learning engineering background.
- Experience building production data and machine learning systems at scale.
- Strong Python skills and experience with Spark or PySpark in production environments.
- Experience supporting machine learning workflows, training pipelines, or feature engineering processes.
- Familiarity with technologies such as Databricks, SageMaker, or Airflow.
- Understanding of cloud-based engineering environments (AWS, Azure, or GCP).
- Nice to have: Experience with recommendation systems or large-scale machine learning workloads.
- Exposure to Kubernetes, Terraform, or AI/LLM workflows and MLOps practices.