ML AWS Professional

• Machine Learning Engineer with experience in designing, building, and deploying end-to-end ML workflows on AWS, utilising SageMaker Pipelines and SageMaker Endpoints.
• Deep understanding of core AWS services including S3, KMS , Lambda , Secrets Manager and CI/CD tools such as CodeBuild and CodePipeline to automate deployments.
• Skilled in orchestrating complex workflows with Airflow and integrating streaming data from Kafka. Short Description of the Job
• Deploy, automate, maintain and monitor machine learning models and algorithms to ensure that that they work effectively in a production environment.

Key Responsibilities

• Understand the needs of business stakeholders, and how machine learning solutions can meet those needs in order to support the achievement of business strategy
• Work collaboratively with colleagues to productionise machine learning models including pipeline design and development, testing and deployment ensuring that the original intent and knowledge is carried over to production
• Create frameworks to ensure robust monitoring of machine learning models within production environment ensuring models are delivering expected quality and performance, understanding and addressing any shortfalls for example through retraining
• Work in an agile way within multi-disciplinary data and analytics teams to achieve agree