Production ML Engineer: Azure & MLOps Architect

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

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