Cloud Engineer

As a Cloud Engineer, you will be responsible for developing and implementing cloud-based artificial intelligence solutions. You will work closely with data scientists, software engineers, and other stakeholders to design, build, and deploy AI models and applications on cloud platforms. Your role will involve leveraging cloud technologies and infrastructure to enable scalable and efficient AI workflows.

Responsibilities:

• Design and develop cloud-based AI solutions: Collaborate with cross-functional teams to understand business requirements and translate them into scalable AI solutions on cloud platforms. Architect, implement, and deploy machine learning models and AI applications.
• Cloud platform selection and configuration: Evaluate different cloud platforms (such as AWS, Azure, or Google Cloud) to determine the best fit for specific AI use cases. Configure and optimize cloud infrastructure to support AI workloads, ensuring high availability, scalability, and performance.
• Data preparation and preprocessing: Work with data scientists and data engineers to identify and access relevant data sources. Develop data pipelines and preprocess data for training AI models, ensuring data quality, consistency, and compliance with data governance standards.
• Model training and optimization: Collaborate with data scientists to train and fine-tune AI models using cloud-based machine learning frameworks. Implement techniques such as distributed training and hyperparameter tuning to optimize model performance and accuracy.
• Model deployment and integration: Deploy trained AI models into production environments on cloud platforms. Integrate AI models into existing systems, applications, or APIs to enable real-time predictions and decision-making.
• Monitoring and maintenance: Monitor deployed AI models and infrastructure for performance, scalability, and reliability. Implement automated monitoring and alerting systems to detect and resolve issues proactively. Maintain and update AI models as needed to adapt to changing business requirements.
• Security and compliance: Ensure the security and compliance of AI solutions on cloud platforms. Implement appropriate security measures, data encryption, access controls, and privacy standards to protect sensitive data and comply with relevant regulations.

Requirements:

• Education and experience: Bachelor's or master's degree in computer science, engineering, or a related field. Previous experience in AI engineering, cloud computing, or related roles is preferred.
• AI and machine learning expertise: Strong understanding of AI and machine learning concepts, algorithms, and frameworks. Experience with popular machine learning libraries (e.g., TensorFlow, PyTorch) and deep learning architectures.
• Cloud platform proficiency: Solid understanding of cloud platforms such as AWS, Azure, or Google Cloud. Familiarity with cloud services related to AI, such as Amazon SageMaker or Azure Machine Learning, is highly desirable.
• Programming skills: Proficiency in programming languages such as Python, Java, or Scala. Experience with scripting and automation using tools like Bash or PowerShell.
• Infrastructure and deployment: Knowledge of cloud infrastructure components (e.g., virtual machines, containers, storage, networking) and experience with infrastructure-as-code tools like Terraform or CloudFormation.
• Data engineering skills: Understanding of data engineering principles, including data preprocessing, ETL (Extract, Transform, Load), and data pipeline development. Proficiency in SQL and database technologies.
• Problem-solving and teamwork: Strong analytical and problem-solving skills to tackle complex AI and cloud engineering challenges. Excellent collaboration and communication skills to work effectively in cross-functional teams.
• Continuous learning mindset: Eagerness to stay updated with the latest advancements in AI, cloud technologies, and best practices. Willingness to learn new tools and technologies as needed.