Join our team as an **AI Platform Engineer** where you will provide vital infrastructure to support machine learning solutions in production. Collaborate with data science teams to develop innovative AI/ML environments on AWS and lead technology processes from concept to delivery. Apply now to be part of advancing AI capabilities in a dynamic environment. **Responsibilities** * Provide infrastructure and platforms to support deployment and monitoring of ML solutions in production * Optimize ML solutions for performance and scalability * Collaborate closely with data science teams to develop AI/ML environments and workflows on AWS * Liaise with R\&D data scientists to help productionise ML pipelines, models, and algorithms * Take responsibility for software engineering from design to implementation, QA, and maintenance * Lead technology processes from concept development to project deliverables completion * Enhance technological stack by working with other teams to adopt advances in Data Processing and AI **Requirements** * Significant experience with AWS cloud environments with 2\+ years in a DevOps or related role * Strong knowledge of AWS services including SageMaker, Athena, S3, EC2, RDS, Glue, Lambda, Step Functions, EKS, and ECS * Experience with DevOps toolchains such as Docker and Git * Proficiency in infrastructure as code technologies like Ansible, Terraform, and CloudFormation * Strong software coding skills with proficiency in Python * Experience managing enterprise platforms and services including handling new client demand and feature requests * Experience with containers and microservice architectures such as Kubernetes, Docker, and serverless approaches * Experience with Continuous Integration and continuous delivery pipelines such as CodePipeline, CodeBuild, and CodeDeploy * GxP compliance experience * Excellent communication, analytical, and problem\-solving skills * English language proficiency at Upper\-Intermediate (B2\) level **Nice to have** * Certification in AWS or related cloud technologies * Experience building large scale data processing pipelines such as Hadoop or Spark * Experience with data science modeling tools such as R and Jupyter notebooks * Multi\-cloud experience including AWS, Azure, or GCP * Experience with mentoring, coaching, and supporting colleagues and clients * Knowledge of SAFe agile principles and practices * Demonstrable experience in building MLOps environments to production standards