1958 AI Engineer (Senior)

Imizizi; Gauteng; On-site; ZAR 600,000 - 900,000; Full time

Job summary

Imizizi in Gauteng, South Africa seeks an experienced data engineer to design, build and operate scalable data pipelines for MLOps and Agent Fabric workloads.

You will integrate diverse data sources, collaborate with ML teams to productionise models, and implement Terraform/Terragrunt to provision platform components. The role emphasizes CI/CD, monitoring, security, and data access enablement in an Agile environment.

Qualifications

• Degree in Data Science, Computer Science, Statistics, Engineering, or equivalent relevant experience.
• Minimum 3–5 years’ experience in data science, AI applications, or related fields with demonstrated stakeholder management experience.
• Proven track record of designing or enabling AI/ML/Data Engineering solutions and working with cross functional delivery teams to deploy them into production.

Responsibilities

• Design, build, and operate scalable, secure data pipelines to support MLOps and Agent Fabric workloads.
• Integrate diverse data sources and ensure robust data ingestion, transformation, and availability for ML teams.
• Collaborate with ML Engineers and AI teams to productionise models and embed data requirements into MLOps pipelines.
• Implement Infrastructure as Code (Terraform/Terragrunt) to provision and manage platform components.
• Build and maintain CI/CD pipelines and automated testing for data and ML deliveries.
• Ensure operational excellence through monitoring, alerting, and logging of data workflows and models.
• Participate in data modelling, schema design, and optimisation for efficient feature storage and retrieval.
• Improve data security and networking posture across the data platform in collaboration with DevOps and security teams.
• Enable business users and analysts by supporting data access patterns, low-code solutions, and documentation.
• Mentor and coach junior data engineers, sharing best practices in data engineering and MLOps.
• Work in an Agile delivery model, contributing to planning, estimation, and delivery of features.
• Evaluate and recommend tools and patterns for supporting agentic systems and AI-driven integrations within the Agent Fabric.

Skills

Data pipelines

MLOps

Cloud platforms

Education

Degree in Data Science / Computer Science / Statistics

Tools

Terraform/Terragrunt

CI/CD

Monitoring & Logging tools

Job description

ROLE & RESPONSIBILITIES:

• Design, build, and operate scalable, secure data pipelines to support MLOps and Agent Fabric workloads.
• Integrate diverse data sources and ensure robust data ingestion, transformation, and availability for ML teams.
• Collaborate with ML Engineers and AI teams to productionise models and embed data requirements into
• MLOps pipelines.
• Implement Infrastructure as Code (Terraform/Terragrunt) to provision and manage platform components.
• Build and maintain CI/CD pipelines and automated testing for data and ML deliveries.
• Ensure operational excellence through monitoring, alerting, and logging of data workflows and models.
• Participate in data modelling, schema design, and optimisation for efficient feature storage and retrieval.
• Improve data security and networking posture across the data platform in collaboration with DevOps and
• security teams.
• Enable business users and analysts by supporting data access patterns, low-code solutions, and documentation.
• Mentor and coach junior data engineers, sharing best practices in data engineering and MLOps.
• Work in an Agile delivery model, contributing to planning, estimation, and delivery of features.
• Evaluate and recommend tools and patterns for supporting agentic systems and AI-driven integrations within the Agent Fabric.

QUALIFICATIONS/EXPERIENCE:

• Degree in Data Science, Computer Science, Statistics, Engineering, or equivalent relevant experience
• Minimum of 3-5 years’ experience in data science, AI applications, or related fields with demonstrated
• stakeholder management experience
• Proven track record of designing or enabling AI/ML/Data Engineering solutions and working with cross functional delivery teams to deploy them into production.