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