Machine Learning Engineer (JHB & CPT)
IT – Analyst, Data Management
Cape Town – Western Cape ~ Johannesburg – Gauteng
BUILD, implement, improve and support the AI platform which will support delivery of the AI strategy as the next Machine Learning Engineer sought by a forward-thinking Financial Services Provider. The ideal candidate must possess a suitable tertiary qualification in Information Technology/Computer Science or similar discipline, have 2+ years’ Software Development including proficiency in Python, SQL, Git, CI/CD, Docker, Kubernetes, Linux, Windows and experience with Machine Learning concepts and frameworks & tools including pandas, numpy, scikit-learn, TensorFlow, Pytorch, Spark MLlib. You will also require experience with modern ETL, compute and orchestration frameworks such as Apache Spark, Apache Flink, Apache Kafka, etc.
- A relevant qualification in Information Technology – Computer Science or Engineering – other.
- Master’s Degree in Information Technology – Computer Science or Engineering – other (ideal or preferred).
- 2+ Years Software Development experience.
- Object Oriented and functional programming in Python.
- Modern Software Development practices.
- Database querying using SQL.
- Data life cycle.
- Machine Learning concepts and model life cycle.
- Strong analytical and problem-solving skills,
- Experience with the modern software development best practices, e.g.
- Agile Software Development
- Code Reviews
- Unit Testing
- Version Control, e.g., Git
- Experience with Microservice Architectures.
- Experience working in an Agile team.
- Experience with ML frameworks and tools (e.g., pandas, numpy, scikit-learn, TensorFlow, Pytorch, Spark MLlib).
- Experience with modern ETL, compute and orchestration frameworks, e.g., Apache Spark, Apache Flink, Apache Kafka, etc.
- Development experience in both Windows and Linux.
- Experience with container technologies, e.g., Docker, Kubernetes.
Nice to haves –
- Experience building Machine Learning or AI systems.
- Proficiency in R language.
- Experience deploying models to production.
- Experience building distributed systems.
- Experience with NoSQL databases.
- Experience working with ML platforms, e.g., MLflow, Kubeflow, etc.
- Experience working with Data Science platforms, e.g., Dataiku, Domino, etc.
- Experience with cloud-based infrastructure, e.g., Azure, AWS, GCP; ideally AWS.
- Data Science lifecycle
- Distributed system design.
- Big Data storage and processing solutions.
- Machine Learning model architectures.
- Analytical skills.
- Decision making skills.
- Planning, organising and coordination skills.
- Research skills.