AI Software Engineer - MLOps Focus
Summary
Builds and maintains MLOps infrastructure for an AI company in Benoni, South Africa — automating ML training, deployment, CI/CD pipelines, and monitoring using Docker, Kubernetes, cloud ML services, and Python/Java/Go. A hybrid, on-site-oriented role requiring 4+ years of software engineering experience.
About the Role
Our client, a forward-thinking company operating at the forefront of **AI & Emerging Technologies**, is seeking a skilled AI Software Engineer with a specialization in MLOps to join their team in **Benoni, Gauteng**. This role is critical in bridging the gap between AI model development and production deployment, ensuring seamless, scalable, and reliable machine learning operations. You will be responsible for building and maintaining robust infrastructure, automating ML pipelines, and implementing best practices for continuous integration, delivery, and monitoring of AI models. The ideal candidate possesses strong software engineering skills combined with a deep understanding of the machine learning lifecycle. This is an excellent opportunity to contribute to innovative AI projects within a thriving tech environment in **Gauteng**, helping to accelerate the deployment and impact of AI solutions. We foster a culture of innovation and technical excellence.
Key Responsibilities
- Design, build, and maintain scalable and reliable MLOps infrastructure and CI/CD pipelines for machine learning models.
- Automate the process of model training, validation, deployment, and monitoring.
- Develop tools and frameworks to support the end-to-end machine learning lifecycle.
- Collaborate with data scientists and AI researchers to understand their needs and facilitate the operationalization of their models.
- Implement robust monitoring, logging, and alerting systems for AI/ML applications.
- Ensure the performance, scalability, and security of deployed AI models and systems.
- Manage containerization (e.g., Docker) and orchestration (e.g., Kubernetes) for ML workloads.
- Troubleshoot and resolve issues related to ML infrastructure and deployment.
- Stay current with emerging MLOps tools, technologies, and best practices.
- Contribute to the development of best practices for AI software engineering and operations.
Requirements
- Bachelor's degree in Computer Science, Software Engineering, or a related technical field.
- Proven experience (4+ years) in software engineering, with a strong focus on MLOps or DevOps for AI/ML projects.
- Proficiency in programming languages such as Python, Java, or Go.
- Experience with cloud platforms (AWS, Azure, GCP) and their associated ML services (e.g., SageMaker, Azure ML, Vertex AI).
- Hands-on experience with containerization technologies (Docker) and orchestration tools (Kubernetes).
- Familiarity with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions).
- Understanding of the machine learning lifecycle and common ML frameworks (e.g., TensorFlow, PyTorch).
- Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack).
- Strong problem-solving and debugging skills.
- Excellent collaboration and communication skills.
Benefits
- Competitive salary and potential for performance bonuses.
- Comprehensive health, dental, and vision insurance.
- Generous paid time off and holiday schedule.
- Opportunities for continuous learning, training, and professional development.
- Exposure to cutting-edge AI and emerging technologies.
- A collaborative and innovative work environment.
- Hybrid work model supporting work-life balance.
- Retirement savings plan.