I am currently hiring for a Senior Manager AWS Sagemaker.

This is a hybrid working position and would require being on site 3 days per week in Nottingham.

Role: Senior Manager AWS Sagemaker

Location: Nottingham, UK

Essential Experience

• Experience in implementing AWS Sage maker into a production environment. Practical understanding of the Sage maker service suite, and how to configure and deploy it
• Experience working in an agile team, comfortable being an SME to support and supplement team delivery capability.
• Create repeatable training workflows to accelerate model development. Catalogue ML artifacts centrally for model reproducibility and governance.
• Integrate ML workflows with CI/CD pipelines for faster time to production. Continuously monitor data and models in production to maintain quality.
• Hands-on experience in Python SQL programming, big data frameworks (Spark, Hadoop etc) Cloud Services AWS Glue, Redshift, Athena, lambda, step functions for ingestion and large data processing.
• Good knowledge on AWS service deployments, AWS infrastructure for MLOps Solid technical writing, presentation, and communication skills.

Qualifications:

• strong xperience, including coding standards, code reviews, source control management, build processes, testing, and operations experience.
• Bachelor's degree in computer science or equivalent
• Data engineering experience to orchestrate, manage AWS infrastructure for Sagemaker, setup data pipelines and manage MLOps

Responsibilities:

• Would join the team as a Sagemaker SME and work alongside them taking the role of a client Architect and AWS Architect to help shape a solution design and implementation plan.
• Securely accelerate ML development and boost productivity with AI-powered developer tools in the IDEs. Generate, debug, and explain the source code with Amazon Code Whisperer and conduct security and code quality scans with Amazon CodeGuru.
• Facilitate upskilling of the team to enable them to accelerate design and implementation of Sage maker.
• Responsible for designing, developing, testing, and deploying distributed machine learning systems and large-scale solutions for our world-wide customer base.
• Collaborate closely with a team of ML scientists to influence our overall strategy and define the team's road map.
• Drive the system architecture, spearhead best practices that enable a quality product, and help coach and develop junior engineers.