Machine Learning Engineer

Nedbank; Johannesburg; On-site; ZAR 700,000 - 1,200,000; Full time

Job summary

Nedbank seeks an experienced ML Engineer to design and operationalise scalable, production-grade machine learning systems across the bank. You will architect robust data/compute infrastructure, establish strong MLOps foundations, and enable high-performance deployments on Azure, Databricks, AKS, Spark, Airflow and MLflow.

The role provides technical leadership enterprise-wide and emphasizes building a scalable ML platform, automating pipelines, and delivering production-ready models with

Qualifications

• 3-7 years’ experience in a data science or cloud-based role.
• Portfolio of delivering projects into production.
• Masters or Doctorate will be an added advantage.

Responsibilities

• Demonstrate proven cloud experience on Azure with strong system/application architecture skills (AKS, Databricks, Spark, Airflow, and MLflow expertise).
• Apply strong data science literacy to bridge model development and production.
• Expert proficiency in programming tools (Python, R, etc.) for data manipulation and model implementation.
• Implement MLOps practices to streamline deployment, monitoring, and governance of models in production.
• Develop and evolve a scalable ML platform meeting community needs and optimize resource usage.
• Automate the end-to-end ML pipeline from data ingestion to deployment, monitoring, and lifecycle management.
• Design and build robust inference systems (APIs, batch processing, real-time streaming).
• Leverage GPU acceleration to enhance performance for deep learning tasks.

Skills

Python

MLOps

Azure

Cloud architecture

SQL/ETL

Data science literacy

Education

STEM Qualification

Masters or Doctorate (advantage)

Tools

Databricks

Spark

Airflow

MLflow

AKS

Azure ML

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

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