Machine Learning Engineer (SC Cleared)

Summary: The role of Machine Learning Engineer (SC Cleared) involves working with a leading global IT and Digital transformation business to deliver a complex cloud solution for a Government client. The position requires strong expertise in Databricks, MLFlow, and MLOps, along with active SC Security Clearance. The contract is for 2 months, with a hybrid working arrangement. The ideal candidate will have a robust background in operationalizing machine learning models.

Key Responsibilities:

• Implementing Databricks best practices in building and maintaining economic modelling (Machine Learning) pipelines.
• Working closely with Data Scientists and operationalizing model with auditing enabled, and ensuring the run can be reproduced.
• Ensuring models are modular, source controlled, and have agreed release numbering.
• Extracting hard-coded elements and parameterising them so model execution can be controlled via input parameters.
• Making sure model input parameters are version controlled + logged to the model execution runs for audit purposes.
• Ensuring model metrics are logged to model runs, model logging, monitoring + alerting to make sure any failure points are captured for the support team to investigate.
• Making sure re-runs of models involve running of multiple experiments + select the best model based on the accuracy and error rate of each experiment.
• Ensuring model is run in line with defined schedule, and that multiple models feeding one another take dependencies into account.
• Capturing data drift, concept drift, model performance degradation signals and ensuring model retrain.
• Defining/maintaining ML Frameworks (Python, R, Matlab templates), and looking for common reusable code that could be used by future models.
• Implementing CI/CD pipelines for ML models and automating deployment.

Key Skills:

• Active SC Security Clearance.
• Strong experience with Databricks, MLFlow, and MLOps.
• Background in Machine Learning Engineering.
• Expertise in operationalizing machine learning models.
• Proficiency in Python, R, and Matlab.
• Experience with CI/CD pipelines for ML models.

Salary (Rate): £550/day

City: London

Country: UK

Working Arrangements: hybrid

IR35 Status: inside IR35

Seniority Level: Mid-Level

Industry: IT

Detailed Description From Employer:

Machine Learning Engineer (SC Cleared)

London (Hybrid)

2 Month Contract

£550/day (Inside IR35)

Machine Learning Engineer needed with active SC Security Clearance, plus strong Databricks, MLFlow and MLOps experience.

The ideal candidate will have a strong background in Machine Learning (ML) Engineering and in-depth expertise in operationalising models in Databricks, MLFlow and MLOps environments.

A chance to work with a leading global IT and Digital transformation business on the delivery of a complex cloud solution programme for a Government client.

Hybrid Working: 2 days/week remote (WFH), and 3 days/week working on-site in the London office. Start ASAP in August 2025.

Key experience + tasks will include:

• Implementing Databricks best practices in building and maintaining economic modelling (Machine Learning) pipelines.
• Working closely with Data Scientists and operationalizing model with auditing enabled, and ensuring the run can be reproduced.
• Ensuring models are modular, source controlled, and have agreed release numbering.
• Extracting hard-coded elements and parameterising them so model execution can be controlled via input parameters.
• Making sure model input parameters are version controlled + logged to the model execution runs for audit purposes.
• Ensuring model metrics are logged to model runs, model logging, monitoring + alerting to make sure any failure points are captured for the support team to investigate.
• Making sure re-runs of models involve running of multiple experiments + select the best model based on the accuracy and error rate of each experiment.
• Ensuring model is run in line with defined schedule, and that multiple models feeding oneanother take dependencies into account.
• Capturing data drift, concept drift, model performance degradation signals and ensuring model retrain.
• Defining/maintaining ML Frameworks (Python, R, Matlab templates), and looking for common reusable code that could be used by future models.
• Implementing CI/CD pipelines for ML models and automating deployment.