Overview

AI Engineer Jobs in Johannesburg, South Africa at NTT Data

Role purpose

The AI Engineer (Full Stack) is responsible for designing, building, and deploying scalable AI-enabled use cases. The role combines backend engineering, data engineering, and Generative AI capabilities to deliver production-grade solutions that integrate seamlessly into enterprise workflows.

The engineer contributes to the development of intelligent capability patterns, including retrieval-based systems, AI agents, and full-stack applications, leveraging a combination of modern cloud platforms, APIs, and AI tooling.

Role summary

This is a hands-on engineering role focused on building real, deployable AI solutions rather than experimentation or isolated prototypes. The engineer operates across the full lifecycle of AI delivery, from data preparation and knowledge engineering to LLM orchestration and application development.

The role requires the ability to work across multiple platforms, including cloud-native environments with an AWS focus and some Azure exposure, and integrate AI capabilities into secure, scalable, and reusable systems. The engineer is expected to contribute to both rapid delivery and the establishment of repeatable engineering patterns.

Responsibilities

Full stack AI solution development

  • Design and develop end-to-end AI applications, including backend services, APIs, and user-facing components
  • Build production-grade solutions that expose AI capabilities through web applications, services, or agents/bots
  • Support authentication, integration, and deployment of AI-enabled applications into enterprise environments

AI and LLM engineering

  • Design and implement LLM-based workflows, including prompt engineering, tool calling, and structured outputs
  • Build and integrate AI agents with defined capabilities, tools, and execution logic
  • Contribute to multi-step reasoning flows and agent-based architectures where required
  • Pattern recognition, identifying where similar use-cases can be adopted in more than one area.

Retrieval and knowledge engineering

  • Develop retrieval-based AI solutions, including vector search and knowledge grounding patterns
  • Transform structured and unstructured data into AI-ready formats, including embeddings and indexed datasets
  • Ensure AI outputs are grounded, explainable, and aligned to defined controls and quality standards

Data engineering and integration

  • Build data pipelines that enable AI systems to interact with enterprise data sources
  • Integrate AI capabilities into core systems using APIs and microservices
  • Support patterns such as text-to-SQL, curated data views, and AI-driven access to structured data

Cloud and platform engineering

  • Develop and deploy AI solutions on cloud platforms, with a focus on AWS (e.g. Lambda, S3, API Gateway, Bedrock, Sage Maker) and exposure to Azure-based services
  • Integrate AI tooling and services into cloud-native architectures using secure and scalable design patterns
  • Apply modern Dev Ops practices, including CI/CD, environment management, and automated deployment pipelines

Engineering standards and delivery

  • Write high-quality, production-grade code in Python using object-oriented and modular design principles
  • Contribute to architecture discussions and support the evolution of reusable AI engineering patterns
  • Ensure solutions are maintainable, testable, and aligned to enterprise engineering standards

Skills

  • Production Grade Python Engineering – ability to build and operate reliable backend services and orchestration layers, object-oriented programming, utilizing CI/CD pipelines etc
  • AI retrieval & grounding engineering – ability to design retrieval pipelines that control hallucination and ensure AI model outputs within CIB Model Risk frameworks and appetite, including explainability and auditability.
  • LLM orchestration & tool integration including engineering prompt flows, function/tool calling, and structured outputs as part of systems, not just ad-hoc prompting. Having built solutions that orchestrate multi-agent frameworks is a plus open‑standard agent integration (MCP)
  • Practical experience implementing Model Context Protocol or equivalent open standards to integrate AI safely with enterprise systems, enterprise API & microservices design
  • Hands-on…

Title: AI Engineer

Company: NTT Data

Location: Johannesburg, South Africa

Category:

 

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