Azure AI Architect

NEXUS CORPORATION (Tokyo, 日本) フォロー 18日前

Full-time Software Engineering Deep Learning Writing GRASP Keras

今すぐ適用する

【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

Requirements

Required:

Bachelor's degree in Computer Science, Software Engineering, or related fields
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 architecture 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

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