AI/Machine Learning Engineer

Job Summary

The AI/ML Engineer supports the development and deployment of scalable and optimized production-ready AI/ML solutions. He/She conducts model experiments, monitors performance, and troubleshoots issues. Proficient in programming, scripting, and statistical methods, the AI/ML Engineer is also expected to apply good practices in responsible AI and data, complying with requirements under the Personal Data Protection Act (PDPA) when handling critical work functions and key tasks.

Critical Work Functions and Key Tasks

• Conduct research on artificial intelligence (AI)/machine learning (ML) models and algorithms.
• Research and implement machine learning algorithms.
• Identify appropriate algorithms based on user requirements.
• Select appropriate datasets and data representation methods.
• Evaluate AI/ML models for production.
• Build and assess AI/ML models.
• Develop codes to package the AI/ML models for scaling.
• Develop AI/ML development pipeline and infrastructure.
• Develop scalable data pipelines to extract, transform, load.
• Scale AI/ML models for production.
• Support continuous improvement of AI solutions.
• Deploy AI/ML models in solutions.
• Test the operation and performance of the deployed models.
• Identify bugs during deployment and create bug fixes.
• Engage in code reviews to improve AI/ML models.
• Perform statistical analysis and fine tuning of the model.
• Prepare documentation to outline data sources, models and results.
• Research and implement machine learning algorithms.

Requirements / Qualifications

Requirements:

• Bachelor’s or master’s degree in computer science, Engineering, or a related field.
• Proven experience as a GenAI Engineer, with a focus on Azure and OpenAI integration. Experience with AWS and Google GenAI services is preferred.
• Strong programming skills in languages such as Python and/or Java.
• Experience with Azure services, including Azure Machine Learning, Azure Cognitive Services, and Azure Functions.
• Familiarity with OpenAI technologies, such as GPT-3/4 and reinforcement learning frameworks.
• Solid understanding of machine learning algorithms and deep learning frameworks.
• Proficiency in data preprocessing, feature engineering, and model evaluation techniques.
• Strong problem-solving and analytical skills.

Preferred Qualifications:

• Experience with AWS services, such as Amazon SageMaker, AWS Lambda, and AWS AI/ML services.
• Knowledge of Google Cloud services, including Google Cloud AI Platform, Google Cloud Functions, and Google Cloud AutoML.
• Experience with cloud-based deployment and scaling of GenAI applications on Azure, AWS, and Google Cloud.
• Knowledge of containerization technologies such as Docker and Kubernetes.
• Familiarity with DevOps practices and tools for CI/CD pipelines.
• Contributions to open-source GenAI or cloud service integration projects.