グローバルでサービスを展開する大手外資系ITサービス企業でのAzure AI Architectの求人

【Responsibilities】
・Envision, build, deploy and operationalize an end-to-end machine learning (ML) and AI pipeline
・Build a robust enterprise wide architecture for AI and collaborate with data scientists, data engineers, developers, operations and security
・Perform the following functions,
-Requirement analysis: Analyzing what an organization needs and how AI can help.
-Solution design: Designing AI solutions that are scalable, cost-effective, and in alignment with the organization’s goals.
-Technology selection: Selecting the appropriate technology stack and tools that will be used to build the AI system.
-Auditing: Conducting a comprehensive audit of AI tools and practices, including data, models, and software engineering, emphasizing continuous improvement. Establishing a feedback loop to evaluate AI services, facilitate model recalibration, and retrain models as needed.
-Implementation: Overseeing the implementation of the AI system and ensuring it meets the organization’s requirements.
-Monitoring and maintenance: Monitoring the performance of the AI system, troubleshooting issues, and ensuring the system is maintained and updated regularly.
・Has a holistic understanding of the business landscape, combined with a grasp of AI capabilities, allowing them to guide AI projects towards success
・To work in team collaboration with cross-functional teams, including technical architects, data engineers, and domain experts, to understand business requirements and develop effective AI solutions
・To be diligent in learning / scaling up in the areas of Data Science-AI with self-initiative towards career excellence

●Required
- Bachelor’s degree in Computer Science, Software Engineering, or related fields (equivalent practical experience also acceptable)
- 5+ years of practical experience in designing and developing AI platforms (Azure is preferred)
- Ability to communicate at a business level in both English and Japanese, and to collaborate with internal and external stakeholders

●Nice to have
- Deep understanding and practical experience in AI-related technologies such as machine learning, deep learning, natural language processing, and computer vision
- AI architecture and pipeline planning. Understand the workflow and pipeline architectures of ML and deep learning workloads. An in-depth knowledge of components and architectural trade-offs involved across the data management, governance, model building, deployment and production workflows of AI is a must.
- Software engineering and DevOps principles, including knowledge of DevOps workflows and tools, such as Git, containers, Kubernetes and CI/CD.
- Data science and advanced analytics, including knowledge of advanced analytics tools (such as SAS, R and Python) along with applied mathematics, ML and Deep Learning frameworks (such as TensorFlow), ML techniques (such as random forest and neural networks) and developing large-scale models using AI frameworks such as TensorFlow, PyTorch, and Keras
- Experience in designing and developing AI systems using cloud platforms (Google, AWS, Azure, etc.)
- Knowledge of AI model operations and deployment (model optimization, monitoring, version control, etc.)
- Practical experience in large-scale data processing technologies (BigQuery, Spark, Hadoop, etc.)
- Knowledge of AI ethics and privacy, and ability to incorporate them into AI system design

●Language
Native level Japanese, ideally Business level English in reading, writing and speaking