Machine Learning Engineer
Hang- Full Time
- Senior (5 to 8 years)
Candidates should possess at least 2 years of Machine Learning engineering experience, focusing on building modern ML infrastructure that covers all stages of a model lifecycle, including development, deployment, management, and monitoring. They should have hands-on experience with AWS, a solid understanding of Infrastructure-as-Code tools such as Terraform and CloudFormation, and desirable exposure to technologies like Python, Spark, Docker, and Kubernetes. Furthermore, candidates must demonstrate the ability to develop across a diverse technology stack and a willingness to learn and adopt new technologies, along with experience participating on cross-functional teams in fast-paced environments.
As a Machine Learning Engineer, the primary responsibilities include empowering Data Science and ML teams by providing the necessary tools and infrastructure for seamless execution of data science and machine learning tasks, supporting the tech stack evolution through development and contributing to the migration into the AWS cloud, leveraging available services, building and maintaining a robust and scalable infrastructure to support pre-computed, batch, and real-time model needs, ensuring smooth deployment and operation of machine learning models, and fostering a culture of innovation by generating and promoting new ideas within the ML Ops domain, finding creative solutions to complex problems, and touching code at various levels, including the UI, backend microservices, database, big data processing, operations, and CD/CI automation.
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