Engineer: AI/ML
Listing reference: woolw_001257
Listing status: Under Review
Apply by: 3 July 2025
Position summary
Industry: FMCG & Supply Management
Job category: FMCG, Retail, Wholesale and Supply Chain
Location: Cape Town
Contract: Permanent
Remuneration: Market Related
EE position: No
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.
