Machine Learning Engineer

Nedbank; Wes-Kaap; On-site; ZAR 900,000 - 1,300,000; Full time

Job summary

Nedbank is seeking an experienced ML Engineer to design, deploy, and operate scalable, production-grade ML systems across the bank. You will architect data and compute infrastructure, establish MLOps foundations, and enable high-performance deployments on Azure, Databricks, AKS, Spark, Airflow and MLflow.

The role emphasizes leading enterprise-wide ML capabilities, collaboration with data science teams, and delivering robust inference APIs and streaming solutions.

Qualifications

• 3–7 years in data science or cloud-based roles.
• Portfolio delivering production ML solutions into production.
• Masters or Doctorate in a STEM field is a plus.

Responsibilities

• Demonstrate proven cloud experience on Azure with AKS, Databricks, Spark, Airflow, and MLflow and enterprise feature store.
• Bridge model development and production with data science literacy and modelling workflows.
• Proficient in Python and/or R for data manipulation, analysis, and production ML tasks.

Skills

Azure cloud

MLOps

Python

R

Data science literacy

GPU acceleration

Education

STEM Qualification

Masters or Doctorate preferred

Tools

Azure Databricks

AKS

Spark

Airflow

MLflow

Python

R

Job description

We are passionate about building scalable, reliable machine learning systems that create real customer impact. Our team blends technical excellence with enjoyment - we learn continuously, solve meaningful problems, and celebrate the solutions we deliver. With several production ML systems already live and more coming, this is a place to grow, contribute, and thrive.

Job Purpose

Apply deep ML engineering expertise to design and operationalise scalable, production-grade machine learning systems across the bank. This includes architecting robust data and compute infrastructure, establishing strong MLOps foundations, and enabling high-performance deployments on Azure, Databricks, AKS, Spark, Airflow and MLflow. The role advances the bank’s ML capabilities and provides technical leadership enterprise wide.

Job Responsibilities

• Demonstrate proven cloud experience on Azure with strong system/application architecture skills (including AKS, Databricks, Spark, Airflow, and MLflow expertise), alongside expert-level knowledge of data structures, algorithms, computability and complexity, and computer architecture, with practical experience using an enterprise feature store.
• Apply strong data science literacy - including understanding of model types, feature engineering, statistical principles, evaluation metrics, and common modelling workflows - to effectively bridge the gap between model development and production.
• Expert proficiency in programming tools (such as Python, R, etc.) for data manipulation, statistical analysis, model implementation, and production grade machine learning tasks is essential.
• Implement MLOps practices to streamline the deployment, monitoring, and management of machine learning models in production, ensuring reproducibility, scalability and model governance.
• Develop, maintain, and evolve a scalable, reliable machine learning platform that meets community and stakeholder needs, proactively resolving performance bottlenecks and optimizing resource usage across compute and storage layers.
• Automate the end-to-end machine learning pipeline, from data ingestion, feature engineering to model deployment, monitoring and lifecycle management.
• Design and build robust inference systems, such as APIs, batch processing, and real-time streaming solutions, to facilitate the deployment and utilization of machine learning models.
• Leverage GPU acceleration to enhance the performance and efficiency of machine learning models, particularly for deep learning tasks.

Qualification

• STEM Qualification
• Engineering, Computer Science, Econometrics, Mathematical Statistics, Actuary Science
• Masters or Doctorate will be an added advantage

Minimum Experience Level

• 3-7 years' experience in a data science or cloud-based role
• Portfolio of delivering projects successfully into production