Responsibility

• Develop highly scalable systems, algorithms, and tools on one platform to supportmachine learning and deep learning solutions,
• Develop, integrate, and optimize end to end AI pipeline,
• Collect, analyze, and synthesize requirements and bottleneck in the technology,systems, and tools used by machine learning engineers and scientists, develop solutions that improve efficiency, leverage more amount of data efficiently,
• Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, Camera with TPU and Nvidia Jatson),
• Explore state-of-the-art deep learning techniques,
• Partner with data science and domain engineering teams to support the business transformation through AI.

Requirements

• University or advanced degree in engineering, computer science, mathematics, or a related field,
• Strong experience working with a variety of relational SQL and NoSQL databases,
• Strong experience working with big data tools: Hadoop, Spark, Kafka, or etc,
• Experience with at least one cloud provider solution (AWS, GCP, Azure),
• Strong experience with object-oriented/object function scripting languages: Python, C++
• Ability to work in a Linux environment,
• Industry experience building innovative end-to-end Machine Learning systems,
• Experience working with distributed systems, service-oriented architectures and designing APIs,
• Strong knowledge of data pipeline and workflow management tools,
• Expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, code reviews, design documentation,
• Relevant working experience with Docker and Kubernetes is a big plus.