Engineer: AI/ML at Woolworths

Introduction

• The AI/ML Engineer will be responsible for operationalizing AI/ML models developed by the Data Science team, ensuring their seamless integration into production environments.
• This role focuses on deploying, monitoring, and maintaining AI/ML models, optimising system performance, and automating MLOps processes to enhance scalability and reliability.

Job description

• Deploy and operationalize AI/ML models developed by the Data Science team into scalable production environments.
• Develop and maintain robust machine learning pipelines to enable efficient model inference and data transformation.
• Ensure seamless integration of AI/ML models with enterprise applications and data systems.
• Implement MLOps best practices, including CI/CD for machine learning, model versioning, monitoring, and automated retraining.
• Optimize AI/ML workflows for performance, cost efficiency, and resilience.
• Collaborate with data engineers to ensure data pipelines support AI/ML model inference and training.
• Leverage cloud-based AI/ML services (AWS SageMaker, Lambda, Glue, etc.) to streamline model deployment and automation.
• Implement AI-driven monitoring and alerting mechanisms to detect model drift and performance degradation.
• Work closely with business stakeholders to ensure AI/ML models are delivering expected value.
• Ensure AI/ML solutions adhere to best practices for security, compliance, and governance.
• Provide technical support and troubleshoot AI/ML model issues in production environments.

Minimum requirements

• Bachelor’s degree in computer science, Engineering, or a related field with 4 - 5 years of experience in operationalising AI/ML models in production environments.
• Strong understanding of AI/ML deployment strategies, including containerization, orchestration, and inference optimisation.
• Experience with implementing MLOps principles, including CI/CD pipelines, automated model monitoring, and retraining.
• Proficiency in Python, with experience in AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Expertise in data engineering tools and frameworks such as Apache Spark, AWS Glue, and SQL.
• Practical experience with AWS services, including SageMaker, Lambda, S3, IAM, and RDS.
• Experience working with structured and unstructured data within enterprise environments.
• Strong software development skills, with experience in version control systems such as Git.
• Familiarity with containerisation and orchestration tools (Docker, Kubernetes) for AI/ML workloads.
• Knowledge of cloud security, data governance, and compliance considerations.
• Excellent verbal and written communication skills; must work well in an agile, collaborative team environment.

ADDITIONAL CRITERIA

• Analytical Mindset: Strong problem-solving ability to optimise AI/ML deployment and system performance.
• Collaboration and Communication: Ability to work closely with data scientists, data engineers, and business stakeholders to ensure seamless AI/ML integration.
• Continuous Learning: Commitment to staying updated on the latest MLOps trends, tools, and automation techniques.
• Innovative Thinking: Proactive in identifying and implementing AI-driven automation and model optimisation improvements.
• Adaptability: Ability to manage multiple AI/ML model deployments and adapt strategies in a fast-paced environment.
• Cultural Fit: Aligns with the organisation’s values, demonstrating integrity, accountability, and a strong work ethic.

Apply by: 3 July 2025