Machine Learning Engineer

Parvana; Kraaifontein; Hybrid; ZAR 700,000 - 1,200,000; Full time

Job summary

Parvana is seeking an experienced ML Engineer to design, train, and deploy models across business areas. You will lead scalable pipelines from data ingestion to deployment, ensuring robust monitoring and integration with core software.

The role requires collaboration with data scientists, software engineers, and product managers to translate business needs into technical solutions. Hybrid work in Cape Town is available.

Qualifications

• 3-5+ years of professional experience building, scaling, and deploying ML models in production.
• Strong proficiency in Python and ML libraries (PyTorch, TensorFlow, scikit-learn, Pandas).
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools.
• Solid understanding of SQL and data manipulation; familiarity with Git and CI/CD pipelines.

Responsibilities

• Design, train, and evaluate ML models (predictive, deep learning, NLP/GenAI) per project needs.
• Lead scalable, automated end-to-end ML pipelines (data ingestion, feature engineering, training, deployment).
• Contribute to ML infrastructure architecture, ensuring containerization and easy integration with core software products.
• Implement continuous monitoring for model performance, data drift, and latency; drive improvements post-deployment.
• Collaborate with Data Scientists, Software Engineers, and Product Managers to translate business requirements into technical solutions.

Skills

Python

PyTorch

TensorFlow

scikit-learn

Pandas

Cloud platforms

MLOps

Git

SQL

Communication

Education

Computer Science / Data Science / ML degree

Tools

Docker

Kubernetes

MLflow

SageMaker

Vertex AI

Job description

Location: Cape Town | Work Type: Hybrid | Job ID: J

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