Machine Learning Engineer Pipeline at Capitec Bank

Purpose Statement

• To build, implement, improve and support the AI platform which will support delivery of the Capitec AI strategy.
• To collaborate in creating and delivering the AI strategy to ensure Capitec is able to compete in a fast changing landscape.  The effective use of AI technologies will be a key strategic differentiator in the future.

 
Experience
Minimum:
Proven experience in:

• 2+ yrs software development experience
• Strong analytical and problem-solving skills
• Expert in Python and SQL
• Experience with the modern software development best practices, e.g.
• agile software development
• code reviews
• unit testing
• version control, e.g. git
• CI/CD
• 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

Ideal:

• Experience in 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

Qualifications (Minimum)

• A relevant qualification in Information Technology - Computer Science or Engineering - Other

Qualifications (Ideal or Preferred)

• Masters Degree in Information Technology - Computer Science or Engineering - Other

Knowledge
Min:
Must have knowledge of:

• Object oriented and functional programming in Python
• Modern software development practices
• Database querying using SQL
• Data life cycle
• Machine learning concepts
• Machine learning model life cycle
• Microservice architectures

Ideal:
Knowledge of:

• Data Science lifecycle
• Distributed system design
• Big data storage and processing solutions
• Machine learning model architectures

Skills

• Analytical Skills
• Decision making skills
• Planning, organising and coordination skills
• Problem solving skills
• Researching skills

Competencies

• Achieving Personal Work Goals and Objectives
• Delivering Results and Meeting Customer Expectations
• Working with People

Conditions of Employment

• Clear criminal and credit record
• A valid driver's license and own vehicle is required
• Contactable via own mobile phone