About the position
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.
Implement monitoring, logging, and telemetry for AI systems.
Establish model versioning, experiment tracking, and release processes (e.g., MLflow).
Ensure AI platforms meet enterprise standards for uptime, scalability, and security.
5. Governance, Responsible AI & Collaboration
Embed Responsible AI principles, data privacy, and compliance into system design.
Work closely with security, data, cloud, and business teams.
Produce architecture diagrams, technical documentation, and operational runbooks.
Provide technical mentorship and engineering leadership.
Required Technical Experience
Must-have
7+ years in software engineering, ML engineering, or cloud engineering roles.
3+ years delivering production AI systems on Microsoft Azure.
Strong hands-on experience with:
Azure OpenAI / Azure AI Services
Azure Machine Learning
Python (primary development language)
Proven experience building:
RAG systems
LLM-powered applications
Secure, API-driven AI platforms
Solid cloud engineering background:
AKS / Kubernetes
CI/CD pipelines
Monitoring & observability
Strong data engineering fundamentals:
Data pipelines
Structured & unstructured data handling
Enterprise integration patterns
Strong Advantage
Microsoft Fabric, Synapse, or Databricks on Azure
Financial services or regulated enterprise environments
Azure AI Engineer Associate or similar certification
Desired Skills:
- Azure AI Engineer
- Senior AI Engineer
- Applied AI Engineer
