Azure Cloud Engineer, Dev Ops
Circa £80K + Excellent Package
Location: London with flexible working options
An excellent opportunity has arisen with a financial services company going through an exciting period of change. The position of Azure Cloud Engineer is to be responsible for build and release engineering of data analytics projects in cloud. To support production systems in cloud and assist in building Machine learning analytics platform in Azure. The successful candidate will be a committed and articulate individual, required to communicate and collaborate effectively with Data Analytics team members and build effective relationships within relevant areas of Technology Services and Data Services team.
Role of the Azure Cloud Engineer
·To be responsible for build and release engineering of data analytics systems and machine learning models in cloud
·Work with data scientists and analytics developers to design and build secure and scalable machine learning platform in Azure.
·To serve as single point of support contact for the application services and deployed models in Azure and being a point of contact for any subsequent queries / issues.
·To liaise with Technology services team/ third parties and resolve production/ cloud environment issues
·To work with Data Analytics team and be responsible for build and release of application changes to cloud
·To build release automation scripts (Devops) for the application/ Machine learning models
·To build docker scripts/containers and manage container releases
·Optimising performance of deployed machine learning models/systems
·Create model monitoring, logging, and alerting for deployed models
·Develop and encourage the adoption of best practices for MLOps, including the development of an MLOps governance framework.
·Conduct internal knowledge sharing sessions to help upskill the wider team on MLOps tooling, usage and best practices
Technical – Required
·5 – 8 Years Software Development Experience
·Strong experience and understanding of software engineering practises, project life cycle and experience working in cloud production environments
·Strong background and proven track record working in Azure
·Experience using and configuring Azure PaaS services like Web Apps, API, SQL PaaS database , Storage Accounts and other Azure services
·Experience working in Azure Batch and distribution of load across multiple instances to improve performance
·Experience working with shared codebase: source control, GIT, source control tools, building continuous integration, Dev Ops pipelines
·Extensive experience working with Docker scripts, containers and deep knowledge of best practices relating to the deployment and security of containerised applications
·Experience in operationalising machine learning projects and deploying ML applications in various forms, including batch and real time inference.
·Experience designing machine learning systems that meet external audit requirements and/ or regulatory requirements.
·Experience setting up machine learning automation pipelines with CI/CD tooling
·Knowledge of Python and Python-based machine learning packages
·Understanding of data science / machine learning concepts, challenges and project lifecycle
Technical – Desirable
·Familiarity with parallel or distributed computing frameworks such as Spark is a plus
·Familiarity with Kubernetes is a plus
·Familiarity with tools such as MLflow, Kubeflow and DVC is a plus