Senior Executive, AI Operations

Our Vision

To transform Maybank into a AI Led Organization by enabling business & leadership with easier, faster, better exposure to AI powered capabilities with special augmentation of Gen AI technology.

Objective

Facilitate the seamless transition of machine learning models from development to production, ensuring that these models are scalable, maintainable, and integrated efficiently within business applications.

Key Responsibilities

• Model Deployment: Automate and streamline the process of deploying machine learning models into production environments, ensuring they run reliably at scale.
• Pipeline Construction: Build and maintain robust data pipelines for continuous training and deployment of machine learning models, including Generative AI models such as LLMs (Large Language Models).
• Monitoring and Maintenance: Implement monitoring solutions to track the performance and health of models in production, quickly identifying and addressing degradation or failures.
• Versioning and Experiment Tracking: Manage version control of both data and models. Use tools like MLflow or DVC to track experiments, manage the lifecycle of machine learning models, and ensure reproducibility.
• Collaboration: Work closely with data scientists, AI researchers, and software engineers to ensure that ML systems are well-integrated with the company’s software infrastructure.
• Performance Optimization: Optimize machine learning infrastructure for performance and cost, utilizing cloud technologies and services efficiently.
• Regulatory Compliance: Ensure that the machine learning deployments comply with relevant data privacy and protection regulations, particularly when handling sensitive or personal data.
• Best Practices and Standards: Establish and promote MLOps best practices within the team, including guidelines for code quality, deployment procedures, and security measures

Requirements & Skills

• Bachelor’s Degree:
• Primary Fields: Computer Science, Data Science, Engineering, or a related field. These disciplines provide foundational skills in software development, algorithms, and systems engineering.
• Coursework: Should include advanced mathematics, statistics, computer programming, machine learning, and possibly courses specifically on MLOps or data engineering.

• Advanced Degree (Optional but advantageous):
• Master’s Degree: Fields like Machine Learning, Data Science, or Artificial Intelligence provide deeper expertise in advanced ML techniques and their applications.
• PhD: A doctorate in a relevant field can be beneficial for roles focusing on cutting-edge research and complex Generative AI deployments.

• Certifications:
• Cloud Certifications: AWS Certified Machine Learning - Specialty, Google Professional Machine Learning Engineer, or Azure AI Engineer Associate reflect expertise in cloud platforms' ML services.
• MLOps Certifications: Certifications focusing on specific tools and platforms like TensorFlow Developer Certificate, Kubeflow, or certifications on specific aspects of data engineering and machine learning operations.

• Technical Skills:
• Programming Skills: Proficiency in Python is typically essential, with knowledge of libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn.
• DevOps Tools: Experience with Docker, Kubernetes, and CI/CD tools (e.g., Jenkins, GitLab CI) for automation and orchestration.
• Data Management: Knowledge of handling big data technologies and databases (e.g., Hadoop, Spark, MySQL, MongoDB).
• Monitoring Tools: Familiarity with monitoring tools like Prometheus, Grafana, or ELK stack to track system performance and health.