Machine Learning Engineer

VIQU IT Recruitment; Greater London; Hybrid; GBP 77,000 - 94,000; Full time

Benefits offered by this job

Bonus up to 25%

Hybrid London office

Job summary

VIQU IT Recruitment is partnering with a leading financial services organisation undergoing a data and technology transformation to build its ML capability. They seek an ML Engineer to design, implement and operate production-grade ML pipelines in a Databricks environment, collaborating with Data Scientists to move models from development to reliable production.

The role is hands-on, focusing on productionisation, deployment and lifecycle management of models, with emphasis on scalable

Qualifications

• Strong commercial experience as an ML Engineer focusing on productionising models.
• Hands-on Python, PySpark and SQL development.
• Experience with Databricks, MLflow and Delta Lake.
• Proven track record building and operating distributed ML pipelines.
• Experience taking Data Science models from notebooks to production.

Responsibilities

• Build and automate end-to-end ML pipelines including feature engineering, training, scoring and deployment.
• Productionise models and transform notebooks into production-ready code.
• Develop scalable ML solutions using Python, PySpark, Databricks and MLflow.
• Deploy models into batch and real-time environments via APIs, scheduled workflows and production pipelines.
• Manage model versioning, promotion and rollback.
• Implement monitoring and observability for production models, including drift and performance alerts.
• Develop automated retraining processes to maintain performance.
• Collaborate with Data Engineering and Platform teams on CI/CD and secure deployment patterns.
• Maintain high engineering standards for testing, documentation and reliability.

Skills

Productionising ML models

End-to-end ML pipelines

Python

PySpark

SQL

Model monitoring

Collaboration with DS

CI/CD experience

Tools

Databricks

MLflow

Delta Lake

Python

PySpark

SQL

Azure DevOps/GitHub Actions

Docker

APIs

Job description

London - Hybrid, 3 days per week in office

Up to £85,000 + Bonus up to 25%

VIQU are partnering with a leading financial services organisation undergoing a significant data and technology transformation, building out its Machine Learning capability across the business. They are seeking an ML Engineer to build, deploy and operate production-grade ML solutions, working closely with Data Scientists to take models from development through to reliable production environments. This is a hands-on engineering role focused on ML pipelines, productionisation, deployment and ongoing model lifecycle management within a modern Databricks environment.

Key Responsibilities of the ML Engineer:

• Build and automate end-to-end ML pipelines covering feature engineering, model training, scoring and deployment
• Productionise models developed by Data Scientists, transforming notebooks and prototypes into modular, tested and production-ready code
• Develop scalable ML solutions using Python, PySpark, Databricks and MLflow
• Deploy machine learning models into batch and real-time environments through APIs, scheduled workflows and production pipelines
• Manage model versioning, promotion and rollback throughout the ML lifecycle
• Implement monitoring and observability across production models, including model and data drift, performance alerts and logging
• Develop automated retraining processes to maintain model performance and reliability
• Work closely with Data Engineering and Platform teams on CI/CD integration, compute optimisation and secure deployment patterns
• Maintain strong engineering standards across testing, documentation, code quality, reproducibility and operational reliability

Key Experience Required of the ML Engineer:

• Strong commercial experience as an ML Engineer, with a clear focus on engineering and productionising machine learning models
• Strong hands-on development skills across Python, PySpark and SQL
• Commercial experience working with Databricks, MLflow and Delta Lake
• Proven experience building and operating distributed data and machine learning pipelines
• Experience taking Data Science models from notebooks or development environments into production
• Strong understanding of model deployment patterns, model lifecycle management and production ML environments
• Experience implementing model monitoring, data/model drift detection, logging and performance monitoring
• Exposure to CI/CD tooling such as Azure DevOps or GitHub ActionsExperience with containerisation, APIs and batch or real-time model deployment
• Ability to collaborate closely with Data Scientists, Data Engineers and Platform teams whilst remaining firmly focused on ML engineering