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

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

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