Data & Analytics Machine Learning Ops Engineer
12 Month Contract
Based in London, 2 Days a week onsite
Day rate up to £600 PD VIA Umbrella, Inside IR35
The ML Ops Engineer will be accountable and responsible for understanding the requirements, ensuring the model is built to production standards, looking at how the model can be deployed, as well as streamlining the processes, automating those processes, and ensuring that we're using the right tools correctly.
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Initially the ML Ops Engineer will be responsible for reviewing the D&A Data Science proof of concept. They will need to understand through the D&A Product Owner the requirements and what the output needs to look like. They will then ensure that the model has been developed in a manner that ports to a production environment. They will provide feedback and guidance on any model changes that would be needed to optimise for production deployments.
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Once the proof of concept phase is over and we move to development the ML Ops Engineer will be accountable for the development and creation of the pipelines needed to deploy the model in to a production environment. Working with the D&A Development team
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The ML Ops Engineer will take the model that has been developed by the D&A Data Science team and ensure that it is accessible. The key areas of responsibility are building, deploying, managing and optimising the model in a production environment, to ensure smooth integration and efficient operations.
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The ML Ops Engineer is responsible for checking deployment pipelines for ML models and triggering CI/CD pipelines. They will need to monitor these pipelines to ensure all tests pass and that the model outputs are generated and sent to the appropriate location. They will review code changes and pull requests from the D&A Data Science team and take these forward in a controlled manner.
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The ML Ops Engineer should enforce security and data governance best practices to safeguard both the models and the data they process.
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The ML Ops Engineer will work to put in place BAU processes that will be adopted by D&A. They will define the process and activity that needs to be undertaken building out a ways of working site for the activity. They will identify and implement monitoring tools to ensure response times of the model are within tolerance. Closely work with D&A Data Science Team for model review, run the code refactoring, containerization, versioning to maintain the quality.
Deliverable
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On boarding and knowledge transfer of Data & Analytics technology patterns and standards.
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Familiarisation with the proposed solution design for the Road User Charging project
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Review of pilot architecture, build, and model serving
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Review of Data Science Model for Secondary ANPR
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Develop and deploy the ML model to production.
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Document ML Ops best practice that fits in with the ways of working
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Training pipeline to a production standard
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Create all necessary technical materials that support the governance processes such as low level design notes, release notes and support guides
Key Knowledge / Skills
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Ability to balance competing tasks and demands effectively, such as ensuring that all assigned development tasks are prioritised and interdependences are worked through with the rest of the development team.
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Effective communication with non-technical stakeholders about complex technicalconcepts to effectively define and prioritise the features, refine the scope.
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Capable at actively listening to, negotiating with and managing conflicts, in order to determine scope and prioritisation for yourself and the team, and to effectively collaborate with stakeholders and other technical roles to identify problems, determine solutions, and effectively manage delivery of an integrated product across multiple development teams and technologies
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Capable at continually assessing and improving product processes within their teams, product areas, and on the wider programme to enhance the efficiency and quality of product development, agile practise and product strategy.
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Solid understanding of machine learning concepts, techniques and frameworks to enable frameworks to be developed.
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Ability to ensure that data scientists can use ML models without having to worry about how they're built or maintained.
Technical experience as an ML Ops Engineer:
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Experience of implementing ML models using the Azure stack.
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Experience in Python and Scala in relation to ML models.
Due to high demand we are only able to respond to applications that meet the required criteria
