Machine Learning Engineer (SC Cleared)

Syntax Consultancy • APPLICATION • London, City, United Kingdom • 19h ago

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/September.

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, 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.

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