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Machine Learning Engineer 10027

Capitec Bank Ltd.

Stellenbosch

On-site

ZAR 600,000 - 800,000

Full time

3 days ago
Be an early applicant

Job summary

A leading financial services provider in Stellenbosch is seeking a Machine Learning Engineer to drive their AI strategy. Collaborating with data scientists, you will enhance operational efficiency through the development of scalable predictive models using Python, SQL, and various machine learning frameworks. An Honors degree and 4+ years of engineering experience are required. Join us in building the best bank through unlocking the potential of our people.

Qualifications

  • 4+ years of experience in any engineering role.
  • Expertise in modern software development best practices.
  • Experience with big data processing frameworks.

Responsibilities

  • Collaborate with data scientists to enhance efficiency.
  • Develop and implement platform improvements.
  • Deliver scalable, robust, production-ready predictive models.

Skills

Python
SQL
TensorFlow
PyTorch
Scikit-learn
XGBoost
Hadoop/Kafka (PySpark)
AWS (S3, Redshift, SageMaker, Bedrock, Lambda)
Github

Education

Honors degree or higher in any field
Job description

Who we are:

We are a bank, but we’re much more than that. We believe that banking is about enabling people to control their financial lives through banking that is simplified, accessible, affordable and delivered through personal experience. By helping our clients manage their financial lives better, we enable them to live better.

Why choose us

At Capitec, we offer our best by living up to our CEO values in every situation – we always put the Client first, act with Energy and take Ownership. And to support people in being their best, our Employee Value Proposition offers every value to all team members through cohesive teams, growth opportunities as well as employee benefits and savings. We make it a priority to ensure that each member of the Capitec team feels welcome, valued, focused, and has the opportunity to grow.

About the role

As a Machine Learning Engineer at Capitec is pivotal in shaping and executing the bank\'s AI strategy. By collaborating with Data Scientists and Decision Scientists, you\'ll enhance their efficiency through the development, implementation, and support of platform improvements. Your contributions will be crucial in positioning Capitec to remain competitive in a rapidly evolving landscape where AI technologies are key strategic differentiators.

This role leverages your deep understanding of machine learning models, from concept to deployment, and expertise in modern software development best practices, big data processing frameworks, and cloud environments whilst sitting in the risk division of the business. You’ll also be working extensively with Python, SQL, and advanced debugging techniques to ensure the delivery of scalable, robust, and production-ready predictive models.

Our ideal candidate has
  • 4+ years of experience in any engineering role
  • Honors degree or higher in any field
  • Python and SQL experience
  • TensorFlow, PyTorch, Scikit-learn, XGBoost, Hugging Face, LangChain, LangGraph, MLflow, RAG, and Transformers
  • Big data frameworks – Hadoop/Kafka (PySpark)
  • Github experience or Git
  • AWS experience: S3, Redshift, SageMaker, Bedrock, Lambda
  • Simplify and make it easy
  • Earn and extend trust
  • Plan, deliver and raise the bar
  • Curious and continuously learns
  • Act, learn and adapt
  • Inspire optimism and persistence
  • Have courage, disagree and commit
Education
  • Completed an Honors Degree or higher

If you are interested in being part of this dynamic team, on a mission to build the best bank in the world through unlocking the potential of its people, please apply. We would love to hear from you!

Capitec is committed to diversity and, where feasible, all appointments will support the achievement of our employment equity goals.

Capitec Bank is an authorised financial services provider (FSP 46669) and registered credit provider (NCRCP13). Capitec Bank Limited Reg. No: 1980/003695/06

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