Senior ML Engineer

Sabenza IT & Recruitment; Johannesburg; On-site; ZAR 900,000 - 1,200,000; Full time

Job summary

Sabenza IT & Recruitment seeks a hands-on ML Platform Engineer in Johannesburg to operationalise models and AI applications on Databricks. You will build scalable pipelines, deploy AI agents and GenAI solutions, and ensure governance and CI/CD practices across production environments.

Ideal candidates have strong Python/SQL, Kubernetes and Docker experience, plus cloud certifications and a track record delivering end-to-end AI use cases.

Qualifications

• Strong understanding of MLOps, DevOps and software engineering practices for ML platforms.
• Experience building, deploying and supporting ML solutions in production.
• Proficiency in Python and experience with SQL and API development.
• Experience with Databricks, MLflow, Model Serving and cloud-native AI/ML platforms.
• Hands-on experience with Kubernetes, Docker and containerised deployment.
• Experience deploying ML solutions on AKS.
• Knowledge of CI/CD pipelines, infrastructure automation and monitoring.
• Experience with distributed computing technologies like Spark.
• Understanding of LLMs, RAG and AI agents.
• Ability to productionise data science solutions and collaborate with Data Scientists.
• Experience delivering end-to-end AI use cases from development to production.
• Ability to translate technical concepts into business outcomes and communicate with stakeholders.
• Strong written and verbal communication; fast-paced environment.

Responsibilities

• Productionise, deploy and monitor ML models and data pipelines on Databricks.
• Build, deploy and support AI Agents, GenAI applications and RAG solutions on Databricks.
• Develop and maintain reusable ML pipelines with CI/CD, testing, monitoring and governance.
• Deploy, optimise and manage models on Azure Kubernetes Service (AKS).
• Design, develop and support APIs and microservices on AKS.
• Implement containerised solutions using Docker and Kubernetes for scalable deployments.
• Monitor model performance, drift and reliability in production.
• Collaborate with Data Scientists to productionise prototypes.
• Work with platform, security, cloud and infra teams for enterprise standards.
• Troubleshoot production issues related to models, pipelines and APIs.
• Optimize AI/ML solutions for performance, cost and reliability.
• Contribute to engineering standards and reusable frameworks.
• Mentor junior engineers and promote knowledge sharing.
• Stay updated with advancements in AI, GenAI, MLOps, Databricks, Kubernetes.

Skills

Databricks

Model Serving

MLflow

Python

SQL

REST APIs

Docker

Kubernetes

CI/CD

MLOps

Machine Learning

Generative AI

LLM deployment

Cloud engineering

Monitoring

Education

Matric and a tertiary qualification

Microsoft Azure certifications (AZ-104, AZ-305, AI-102 or equivalent)

Databricks certifications (Data Engineer, ML Engineer, GenAI Engineer)

Kubernetes/Containerisation certifications (CKA/CKAD)

DevOps/MLOps or Platform Engineering certifications

AWS or Google Cloud certifications advantageous

Machine Learning / Data Science certifications from providers (MS, Databricks, SAS, Coursera, DeepLearning.AI)

Tools

Databricks

MLflow

Kubernetes

Docker

AKS

Job description

Johannesburg, South Africa

Build, deploy, scale and support machine learning, AI and GenAI solutions in production. The role focuses on operationalising models, developing AI applications and agents, and creating the platforms and services required to deliver business value at scale.

Responsibilities

• Productionise, deploy and monitor machine learning models and data science pipelines on Databricks.
• Build, deploy and support AI Agents, GenAI applications and RAG solutions on Databricks.
• Develop and maintain reusable ML pipelines using MLOps principles, including CI/CD, automated testing, monitoring and governance.
• Deploy, optimise and manage open source AI and machine learning models on Azure Kubernetes Service (AKS).
• Design, develop and support custom APIs and microservices on AKS to expose AI and machine learning capabilities to business applications.
• Implement containerised solutions using Docker and Kubernetes to ensure scalable, secure and resilient deployments.
• Monitor model performance, drift, reliability and operational health in production environments.
• Partner with Data Scientists to productionise prototypes and enable business-ready solutions.
• Collaborate with platform, security, cloud and infrastructure teams to ensure compliance with enterprise standards.
• Troubleshoot and resolve production issues related to models, pipelines, APIs and AI applications.
• Optimise AI and ML solutions for performance, scalability, cost and reliability.
• Contribute to engineering standards, reusable frameworks and best practices across the AI and ML ecosystem.
• Mentor junior engineers and promote knowledge sharing across the team.
• Stay current with advancements in AI, GenAI, MLOps, Databricks, Kubernetes and cloud technologies.
• Production-ready ML models and pipelines running on Databricks.
• AI Agents and business applications deployed on Databricks.
• Open source LLMs and AI services deployed on AKS.
• Secure and scalable APIs exposing AI capabilities to consuming systems.
• Automated deployment, monitoring and governance processes.
• Reliable, scalable and compliant AI platforms supporting business outcomes.

Skills

• Databricks Workflows, Model Serving, MLflow and Mosaic AI
• Python, SQL and REST APIs
• Docker and Kubernetes
• CI/CD and MLOps practices
• Machine Learning and Generative AI
• LLM deployment and optimisation
• Cloud engineering and infrastructure automation
• Monitoring, observability and troubleshooting

Qualifications

• Matric and a Tertiary Qualification
• Microsoft Azure certifications (AZ-104, AZ-305, AI-102 or equivalent)
• Databricks certifications (Data Engineer, Machine Learning Engineer, Generative AI Engineer)
• Kubernetes and containerisation certifications (CKA, CKAD or equivalent)
• DevOps, MLOps or Platform Engineering certifications
• AWS or Google Cloud certifications will be advantageous
• Machine Learning, Artificial Intelligence or Data Science certifications from recognised providers such as Microsoft, Databricks, SAS, Coursera or DeepLearning.AI will be an added advantage

Technical / Professional Knowledge

• Strong understanding of MLOps, DevOps and software engineering practices for machine learning platforms.
• Experience building, deploying and supporting machine learning solutions in production environments.
• Proficiency in Python and experience with SQL and API development.
• Experience with Databricks, MLflow, Model Serving and cloud-native AI/ML platforms.
• Hands-on experience with Kubernetes, Docker and containerised application deployment.
• Experience deploying and supporting machine learning and Generative AI solutions on Azure Kubernetes Service (AKS).
• Knowledge of CI/CD pipelines, infrastructure automation and platform monitoring.
• Experience with distributed computing technologies such as Spark and large-scale data processing frameworks.
• Understanding of machine learning, large language models (LLMs), retrieval-augmented generation (RAG) and AI agents.
• Ability to productionise data science solutions and collaborate effectively with Data Scientists.
• Experience delivering end-to-end AI and machine learning use cases from development to production.
• Ability to translate technical concepts into business outcomes and communicate effectively with stakeholders.
• Strong written and verbal communication skills with the ability to work across cross-functional teams.
• Self-driven, adaptable and capable of thriving in a fast-paced, technology-driven environment.