At a Glance
- Tasks: Design and build scalable data pipelines for machine learning solutions.
- Company: Rapidly growing financial services business investing in data and ML capabilities.
- Benefits: Hybrid working, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with exposure to cutting-edge technologies.
- Why this job: Join a high-calibre team and shape the future of machine learning at scale.
- Qualifications: Strong background in software, data, or machine learning engineering.
The predicted salary is between 60000 - 80000 £ per year.
We are partnering with a rapidly growing financial services business that is investing heavily in its machine learning and data platform capabilities. This is a hands-on engineering role focused on helping Data Science teams build, deploy, and scale machine learning solutions in production. The work sits at the intersection of data engineering, machine learning engineering, and MLOps, with a strong focus on production systems, feature engineering, workflow orchestration, and model lifecycle management.
What you'll be doing:
- 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, Databricks Workflows, or similar technologies.
- Support model training, deployment, monitoring, and experiment tracking in production environments.
- Contribute to feature engineering, feature management, 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.
What we're looking for:
- Strong software, data, or machine learning engineering background.
- Experience building production data and machine learning systems at scale.
- Experience with Spark or PySpark in production environments.
- Experience supporting machine learning workflows, training pipelines, or feature engineering processes.
- Exposure to technologies such as Databricks, SageMaker, Azure ML, Vertex AI, MLflow, Airflow, feature stores, or similar platforms.
- Experience working closely with Data Scientists to deploy and operate machine learning solutions.
- Strong understanding of cloud-based engineering environments (AWS, Azure, or GCP).
Nice to have:
- Experience with recommendation systems, forecasting, optimisation, or other large-scale machine learning workloads.
- Experience with Kubernetes, Terraform, or cloud infrastructure automation.
- Exposure to modern AI/LLM workflows and MLOps practices.
This is an opportunity to join a high-calibre engineering team and play a key role in shaping how machine learning is delivered and operated at scale.
Senior Data & ML Engineer in London employer: DW Search
Join a dynamic and rapidly growing financial services business in London, where innovation meets opportunity. As a Senior Data & ML Engineer, you'll be part of a collaborative culture that values hands-on engineering and offers extensive growth opportunities in machine learning and data platforms. With a hybrid working model and a commitment to employee development, this role provides a unique chance to shape the future of machine learning solutions in a supportive and forward-thinking environment.
