Overview
In this role you will build and operate production-grade ML solutions within a data-driven transformation program. You will collaborate with Data Scientists to move models from development to reliable production environments, focusing on ML pipelines, deployment, and lifecycle management in a modern Databricks setup. You’ll shape scalable ML systems, enable real-time and batch scoring, and ensure observability and reliability across production models. This is a hands-on engineering role with a strong impact on the company’s ML capability and data strategy.
Responsibilities
- Build and automate end-to-end ML pipelines (feature engineering, training, scoring, deployment)
- Productionise models from Data Scientists into modular, tested code
- Develop scalable ML solutions using Python, PySpark, Databricks and MLflow
- Deploy models into batch and real-time environments via APIs and workflows
- Manage model versioning, promotion and rollback across the lifecycle
- Implement monitoring and observability for production models (drift, performance, logging)
- Create automated retraining to maintain model performance
- Collaborate with Data Engineering and Platform teams on CI/CD, compute optimisation, secure deployment
- Maintain high engineering standards (testing, documentation, reproducibility, reliability)
Key requirements
- Strong hands-on ML engineering with productionising focus
- Proficient in Python, PySpark, SQL
- Commercial experience with Databricks, MLflow and Delta Lake
- Experience building and operating distributed data/ML pipelines
- Experience moving Data Science models from notebooks to production
- Understanding of deployment patterns and ML lifecycle in production
- Experience with model monitoring, drift detection, logging, performance monitoring
- Exposure to CI/CD tools such as Azure DevOps or GitHub Actions
- Experience with containerisation, APIs and batch/real-time deployment
- Ability to collaborate with Data Scientists, Data Engineers and Platform teams
- Collaborative cross-functional work
- Strong problem-solving and communication
- Attention to quality, documentation and reliability
- Python
- PySpark
- SQL
ML Engineer employer: VIQU Limited
Join a leading Investment Banking firm in London, where you will thrive in a dynamic hybrid work environment that fosters innovation and collaboration. With a strong emphasis on employee growth, you will have access to continuous learning opportunities and the chance to work on complex projects that make a real impact in the financial sector. Our inclusive work culture values your contributions and encourages you to take ownership of your projects, ensuring a rewarding and meaningful career path.
