Senior Machine Learning Engineer

Machine Learning Engineer

Location: Johannesburg or Cape Town, South Africa

Work Arrangement: Hybrid – 2 to 3 days per week in office

Experience: 4–7 years

Employment Equity: EE candidates preferred

ROLE OVERVIEW

We are seeking a Machine Learning Engineer to join an Enterprise Data Science / Group Technology environment. The successful candidate will be responsible for taking machine learning and data science solutions from development through to production.

The role requires a strong combination of Data Science, Machine Learning, Software Engineering, DevOps and Platform Engineering capabilities.

The ideal candidate will understand machine learning concepts and data science applications while also having hands-on experience with Kubernetes, Azure Kubernetes Service (AKS), Databricks, CI/CD, automation and production deployment environments.

KEY RESPONSIBILITIES

• Develop, deploy and maintain machine learning and data science solutions in production environments.
• Work across data science, software engineering and platform engineering teams.
• Design and implement production-ready machine learning solutions.
• Develop and maintain automated deployment and CI/CD pipelines.
• Deploy and manage applications and machine learning workloads using Kubernetes and Azure Kubernetes Service (AKS).
• Work with Databricks for data engineering, data science and machine learning workloads.
• Develop software and automation using strong programming and software engineering practices.
• Build and integrate APIs and production services.
• Support deployment, infrastructure and platform requirements for machine learning applications.
• Implement DevOps and MLOps practices across the machine learning lifecycle.
• Support model deployment, monitoring and operationalisation.
• Work with data ingestion and real-time data pipelines.
• Collaborate with Data Scientists, Software Engineers, Platform Engineers, DevOps Engineers and other technical stakeholders.
• Troubleshoot and improve production machine learning systems.
• Contribute to system design and scalable production architectures.

ESSENTIAL REQUIREMENTS

Data Science & Machine Learning

• Strong understanding of Data Science and Machine Learning concepts.
• Understanding of machine learning models and how data science applications are developed and operationalised.
• Practical experience working with machine learning solutions.

Platform Engineering

Hands-on experience with:

• Kubernetes
• Azure Kubernetes Service (AKS)
• Databricks
• Cloud/platform environments
• Infrastructure and deployment environments
• Application and workload deployment

Software Engineering & Production Deployment

• Strong Software Engineering background.
• Strong programming experience.
• Experience taking applications, data science solutions or machine learning models into production.
• Experience with CI/CD.
• Experience with DevOps practices.
• Experience developing and maintaining automation and deployment pipelines.
• Experience working with APIs.
• Understanding of production environments and deployment processes.

ADVANTAGEOUS SKILLS

Experience with any of the following will be advantageous:

• Python
• Docker
• MLOps
• MLflow
• JupyterHub
• Model monitoring
• Data ingestion pipelines
• Real-time data pipelines
• System design
• Feature stores
• Microsoft Azure
• Cloud-native technologies
• Machine learning model lifecycle management

Additional technologies can be learned where the candidate demonstrates a strong core foundation in Machine Learning, Software Engineering and Platform Engineering.

REQUIRED EXPERIENCE

• Approximately 4–7 years of relevant professional experience.
• Demonstrated experience delivering technical solutions into production.
• Experience working with machine learning or data science applications.
• Experience working with Kubernetes/AKS and Databricks.
• Experience in software engineering, DevOps or platform engineering environments.

IDEAL CANDIDATE PROFILE

The ideal candidate demonstrates a combination of:

Data Science + Machine Learning + Software Engineering + Platform Engineering

The candidate should be able to understand a machine learning or data science solution and contribute to the technical work required to build, deploy, automate, operate and maintain that solution in a production environment.

CANDIDATES TO AVOID

Candidates should generally not be prioritised where their experience is limited to:

• Pure Data Science with little or no production deployment experience.
• Pure Platform Engineering without an understanding of Machine Learning or Data Science applications.
• AI tools such as Microsoft Copilot, Power Automate or similar technologies without a strong Machine Learning Engineering foundation.
• Limited or no experience with Kubernetes, AKS or Databricks.
• Development experience without exposure to production deployment, CI/CD or DevOps.
• Repeated short-term employment periods, particularly multiple stays of approximately 6–8 months, unless there is a clear explanation for the movements.

When reviewing a CV, assess the following:

Can this candidate take a Machine Learning or Data Science solution and successfully deploy it into production while understanding the underlying software engineering, platform and infrastructure requirements?

Candidates who can demonstrate this combination should be prioritised for further screening.