Machine Learning Engineer

Parvana

Job Location:

Cape Town - South Africa

Monthly Salary: Not Disclosed

Posted on: 4 days ago

Vacancies: 1 Vacancy

Job Summary

Location: Cape Town Work Type: Hybrid Job ID: J107221

About our client:

Our client a reputable financial services firm listed on the Johannesburg Stock Exchange (JSE) holds a strong belief in the transformative power of incremental progress. They are committed to empowering individuals and businesses alike offering tailored solutions and expert guidance to help pave the way to success. With a focus on innovation and excellence they provide secure financial futures through personalised advice and cutting-edge products. Their mission is to support diverse goals and aspirations enriching lives across every journey.

What you will be doing:

Design train and evaluate machine learning models (predictive deep learning or NLP/GenAI depending on project needs) to solve complex business problems.
Lead the development of scalable robust and automated end-to-end ML pipelines (data ingestion feature engineering training and deployment).
Contribute to the architecture of our ML infrastructure ensuring models are containerized deployed efficiently and easily integrated into core software products.
Implement continuous monitoring strategies to track model performance data drift and latency driving iterative improvements post-deployment.
Conduct experimentation to evaluate new algorithms open-source frameworks and methodology advancements.
Partner with Data Scientists Software Engineers and Product Managers to translate business requirements into technical solutions.

What our client is looking for:

A relevant tertiary qualification would be beneficial (Computer Science Data Science Machine Learning etc.).
3 - 5 years of professional experience developing scaling and deploying ML models in a production environment.
Strong proficiency in Python and ecosystem libraries (e.g. PyTorch TensorFlow scikit-learn Pandas).
Hands-on experience with cloud platforms (AWS GCP or Azure) and MLOps tools (e.g. Docker Kubernetes MLflow or cloud-native equivalents like SageMaker/Vertex AI).
Solid understanding of statistical modelling feature engineering and data manipulation skills (including strong SQL).
Familiarity with software engineering best practices including Git version control CI/CD pipelines and writing clean testable code.
Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.

For a more comprehensive list of opportunities that we have on offer do visit our website - PyTorch TensorFlow scikit-learn Pandas AWS GCP Azure Docker Kubernetes MLflow SageMaker Vertex AI SQL Git CI/CD MLOps Deep Learning NLP GenAI

Required Skills:

Python PyTorch TensorFlow scikit-learn Pandas AWS GCP Azure Docker Kubernetes MLflow SageMaker Vertex AI SQL Git CI/CD MLOps Deep Learning NLP GenAI

Location: Cape Town Work Type: Hybrid Job ID: J107221About our client:Our client a reputable financial services firm listed on the Johannesburg Stock Exchange (JSE) holds a strong belief in the transformative power of incremental progress. They are committed to empowering individuals and businesse...

Location: Cape Town Work Type: Hybrid Job ID: J107221

About our client:

What you will be doing:

Design train and evaluate machine learning models (predictive deep learning or NLP/GenAI depending on project needs) to solve complex business problems.
Lead the development of scalable robust and automated end-to-end ML pipelines (data ingestion feature engineering training and deployment).
Contribute to the architecture of our ML infrastructure ensuring models are containerized deployed efficiently and easily integrated into core software products.
Implement continuous monitoring strategies to track model performance data drift and latency driving iterative improvements post-deployment.
Conduct experimentation to evaluate new algorithms open-source frameworks and methodology advancements.
Partner with Data Scientists Software Engineers and Product Managers to translate business requirements into technical solutions.

What our client is looking for:

A relevant tertiary qualification would be beneficial (Computer Science Data Science Machine Learning etc.).
3 - 5 years of professional experience developing scaling and deploying ML models in a production environment.
Strong proficiency in Python and ecosystem libraries (e.g. PyTorch TensorFlow scikit-learn Pandas).
Hands-on experience with cloud platforms (AWS GCP or Azure) and MLOps tools (e.g. Docker Kubernetes MLflow or cloud-native equivalents like SageMaker/Vertex AI).
Solid understanding of statistical modelling feature engineering and data manipulation skills (including strong SQL).
Familiarity with software engineering best practices including Git version control CI/CD pipelines and writing clean testable code.
Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.