We are looking for a **Senior AI Platform Engineer** to deliver robust infrastructure supporting production ML solutions on AWS, enhancing AI/ML workflows and platforms. You will collaborate with data science teams to architect innovative AI/ML environments using AWS services like SageMaker and EKS, driving projects from inception to delivery. Join us to advance AI technology within a forward\-thinking team. **Responsibilities** * Deliver infrastructure and platforms to enable deployment and monitoring of ML solutions in production * Improve ML solution performance and scalability on AWS * Work closely with data science teams to build AI/ML workflows and environments leveraging AWS services * Coordinate with R\&D data scientists to operationalize ML pipelines, models, and algorithms * Own software engineering lifecycle from design through implementation, testing, and maintenance * Drive technology initiatives from initial concept through to project completion * Collaborate with cross\-functional teams to integrate advances in Data Processing and AI into the technology stack **Requirements** * Extensive experience working with AWS cloud infrastructure, including 3\+ years in DevOps or related roles * Proficient knowledge of AWS services such as SageMaker, Athena, S3, EC2, RDS, Glue, Lambda, Step Functions, EKS, and ECS * Hands\-on experience with DevOps tools including Docker and Git * Strong skills in infrastructure as code using Terraform, Ansible, and CloudFormation * Advanced programming ability in Python * Background managing enterprise platforms and responding to client demands and feature requests * Experience with containerization and microservices architectures like Kubernetes, Docker, and serverless systems * Proven experience with CI/CD pipelines including CodePipeline, CodeBuild, and CodeDeploy * Familiarity with GxP compliance standards * Excellent communication, analytical thinking, and problem\-solving capabilities * English proficiency at Upper\-Intermediate (B2\) level **Nice to have** * AWS or related cloud technology certifications * Familiarity with large\-scale data processing frameworks such as Hadoop or Spark * Experience using data science tools like R and Jupyter notebooks * Exposure to multi\-cloud environments including AWS, Azure, or GCP * Skills in mentoring, coaching, and supporting peers and clients * Understanding of SAFe agile methodologies * Practical experience building MLOps environments ready for production