Machine Learning Engineer (SC Cleared)

Reference
-21016C
Employer
Syntax Consultancy Ltd
Contract Type
Fixed Term
Speciality
IT & Telecoms, IT Contractor
Location
City Of London, England, London, West End
Town/City
London
Benefits
£550.00 Per Annum 550/day (Inside IR35)
Salary
£550 Per Annum
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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.