• Provide the vital infrastructure and platform to support the deployment and monitoring of ML solutions in production Optimizing solutions for performance and scalability.
• Collaborate closely with data science teams in developing cutting edge data science, AI/ML environments and workflows on AWS.
• Liaise with R&D data scientists to understand their challenges and work with them to help productionise ML pipelines, models, and algorithms for innovative science.
• Take responsibility for all aspects of software engineering, from design to implementation, QA and maintenance.
• Lead technology processes from concept development to completion of project deliverables.
• Liaise with other teams to enhance our technological stack, to enable the adoption of the latest advances in Data Processing and AI.

• Significant experience with AWS cloud environments is essential. Knowledge of SageMaker, Athena, S3, EC2, RDS, Glue, Lambda, Step functions, EKS, and ECS is also essential. Certification in appropriate areas will be viewed favourably.
• Modern DevOps mindset, using best-of-breed DevOps toolchains, such as Docker and Git.
• Experience with infrastructure as code technology such as Ansible, Terraform and Cloud Formation.
• Strong software coding skills, with proficiency in Python, however exceptional ability in any language, will be recognized.
• Experience managing an enterprise platform and service, handling new customer demand and feature requests.
• Experience with containers and microservice architectures e.g., Kubernetes, Docker and serverless approaches.
• Experience with Continuous Integration and building continuous delivery pipelines, such as CodePipeline, CodeBuild, and Code Deploy.
• GCP experience.
• Excellent communication, analytical and problem-solving skills.

Desirable Skills/Experience