Ml Engineer

Role & responsibilities Partners with Architecture/Product/CloudOps/Engineering teams to craft highly scalable, flexible and resilient cloud architectures that address customer business problems and accelerate the adoption of cloud services. Designs and implements complex architectural solutions using AWS design principles, best practices, and industry standards. Build scalable, reliable, and cost-efficient ML pipelines using Python, AWS services (SageMaker, Lambda, Step Functions, S3, ECR, etc.), and container technologies (Docker, ECS/Fargate). Lead technical design reviews, guide engineering teams on architectural best practices, and create high-level and low-level design documents. Determines code quality and test coverage, designs and implements tests to make sure software is built to the highest quality possible. Communicate and explain technical/architectural decisions to product, development, and delivery teams Drive continual improvement in quality and efficiency, including defect prevention/root cause analysis, as well as suggest and adopt improvements to technology and efficiency. Perform proof of concept work for integrating new technologies into the existing product. Ability to comprehend detailed project specifications, as well as the ability to adapt to various technologies and simultaneously work on multiple projects. Participates in reviews of software engineers code to deliver high-quality solutions. Work closely with the product and actively participate in business requirement analysis. Lead and mentor junior members of the team. Research and implement performance tuning and enhancements to existing and newly developed systems to gain the most performance from the existing Infrastructure. Skills AWS SageMaker, Amazon Lambda, Python, AWS Architecture, AWS Machine Learning, Docker, Stakeholder Communication, ECS Fargate, Analysis and Design