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Machine Learning Engineer ANPR System

Salt Search
Posted 12 hours ago, valid for 8 days
Location

London, Greater London EC1R 0WX

Salary

£45,000 - £54,000 per annum

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Contract type

Full Time

In order to submit this application, a Reed account will be created for you. As such, in addition to applying for this job, you will be signed up to all Reed’s services as part of the process. By submitting this application, you agree to Reed’s Terms and Conditions and acknowledge that your personal data will be transferred to Reed and processed by them in accordance with their Privacy Policy.

Sonic Summary

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  • Salt is seeking a Machine Learning Ops Engineer for a 12-month Inside IR35 contract with a local government client in South East London.
  • The role is primarily hybrid, requiring 2 days a week in the office, focusing on enhancements to the ANPR (Automatic Number Plate Recognition) system.
  • Candidates should have experience working with ANPR systems and strong expertise in Python and Scala, along with proficiency in machine learning libraries such as PyTorch and XGBoost.
  • Proven experience in deploying ML models in production, optimizing performance, and knowledge of MLOps best practices is essential.
  • The position offers a competitive salary, and candidates are expected to have relevant experience in MLOps and cloud technologies, particularly Azure.

Salt are recruiting for an immediate requirement with a local government client of ours who is looking for a Machine Learning Ops Engineer (Data & Analytics).

This will be an Inside IR35 contract for 12 months initially. It would be a mainly hybrid role with 2 days a week expected in office, based in South East London.

Your main focus of the project will be around the ANPR (Automatic Number Plate Recognition) system. You will be responsible in delivering enhancements and optimizations to our client's secondary ANPR system so that it is fully refined.

Key experiences:

- Experience working on ANPR systems is required.

- Strong expertise in Python and Scala, with proficiency in machine learning libraries (e.g., PyTorch, ONNX, XGBoost).

- Proven experience deploying ML models in production and optimizing their performance.

- Knowledge of MLOps best practices, including model development, deployment, and monitoring.

- Expertise in building MLOps pipelines on Azure Cloud (Azure DevOps, Functions, ML, Databricks, CosmosDB).

- Familiarity with CI/CD principles, version control (Git, MLFlow), and automated testing.

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In order to submit this application, a Reed account will be created for you. As such, in addition to applying for this job, you will be signed up to all Reed’s services as part of the process. By submitting this application, you agree to Reed’s Terms and Conditions and acknowledge that your personal data will be transferred to Reed and processed by them in accordance with their Privacy Policy.