AI Engineer - Azure

SUMMARY:
Senior Azure AI Engineer to design and operate production-grade AI and Generative AI systems

POSITION INFO:

We are seeking a senior, hands-on Azure AI Engineer to design, build, and operate production-grade AI and Generative AI systems.

This is not a research or modelling-only role.
This person will be responsible for engineering and deploying AI solutions end-to-end on Microsoft Azure - from data ingestion and system architecture through to secure APIs, model deployment, monitoring, and optimisation.

The successful candidate will act as a technical owner of AI solutions, working closely with data teams, cloud engineers, and business stakeholders to deliver enterprise-scale, compliant, and reliable AI platforms.

Key Outcomes of the Role

• Design and deliver production AI and GenAI systems on Azure

• Build and own RAG-based solutions and LLM-powered applications

• Establish deployment, monitoring, and MLOps standards

• Integrate AI solutions securely into Nedbank's enterprise environment

• Improve performance, reliability, cost control, and observability of AI workloads

• Produce clear architecture documentation, runbooks, and best practices

Core Responsibilities

1. AI Solution Architecture & Delivery

• Design and implement AI solutions using:

  • Azure OpenAI

  • Azure AI Studio / Azure Machine Learning

  • Azure AI Search

• Lead the end-to-end delivery of AI systems from proof-of-concept to production.

• Architect scalable, secure AI platforms aligned to enterprise and regulatory requirements.

2. Generative AI & RAG Systems

• Build and optimise Retrieval-Augmented Generation (RAG) pipelines.

• Engineer prompt frameworks, orchestration layers, and evaluation processes.

• Improve response quality, latency, security, and cost efficiency.

3. Cloud Engineering & Integration

• Develop secure APIs and event-driven integrations using:

  • Azure API Management

  • Event Hub / messaging services

• Containerise and deploy AI workloads to:

  • AKS, Azure Functions, and App Services.

• Implement Infrastructure as Code (Bicep, ARM, or Terraform).

4. MLOps, Reliability & Observability

• Build CI/CD pipelines for AI and ML workloads.

• Impl