ML Ops Engineer

ML Ops Engineer 12 Month Contract Day rate £400 per day, inside ir35 Hybrid – mainly home working, occasional London visit (Full project scope available) Working on two significant London authority projects - Road User Charging, which includes. The Congestion Charge (CC) The Low Emission Zone (LEZ) The Ultra-Low Emission Zone (ULEZ) And Automatic Number Plate Recognition (ANPR). The scope of the project will be to: Review the existing Data Science model and propose model improvements. Review the proposed architecture and propose improvements whilst considering our strategy and costs. Document CI/CD for a machine learning models within the controls of Data and Analytics. Build a backlog of activity that needs to be completed to deliver the solution. As a development team build out the solution. Fully test out model deployments and document the process clearly. Deploy ML model and ensure it is operational by the expected date. You will be working for this large Government Agency, alongside data scientists, data engineers and domain experts to collect requirements and establish project goals and document the processes. You will study and transform data science prototypes, applying the appropriate machine learning algorithms and tools Implement and optimise machine learning algorithms using programming languages such as Python, and Scala. Utilise machine learning libraries and frameworks such as PyTorch, ONNX, and XGBoost. Evaluate model performance, conduct A/B testing, and iteratively improve model accuracy and efficiency. Implement machine learning models in production environments and oversee their performance using relevant metrics. Implement MLOps best practices to improve the development, deployment and monitoring of ML models Provide advice and guidance on ML best practices Document machine learning processes, methodologies, and results to facilitate knowledge sharing and collaboration. Your essential skills Experience as a ML Ops Engineer is essential Experience of developing and implementing ways of working. Experience in Developing and enforcing best practices for machine learning lifecycle management using Azure Databricks. Good to have skills in containerization like Docker and ACR High-level expertise in the Python programming language including PySpark, Proficiency in utilising machine learning libraries and frameworks like PyTorch, ONNX, and XGBoost. Strong understanding of software testing and CI/CD principles and version control (Git, MLFlow) for automated deployment of machine learning systems Familiarity with our systems (Azure cloud platform covering Azure DevOps Pipelines, Azure Functions, AzureML, Azure Databricks, CosmosDB) is desirable Excellent problem-solving skills and analytical thinking. Strong communication and collaboration skills.