Overview

Lead AI Engineer Jobs in Johannesburg, South Africa at Samaha Consulting

Overview

  • Own AI engineering architecture and technical decision-making across multiple priority use cases.
  • Define reusable patterns for copilots, agents, RAG, knowledge assistants, workflow automation and AI-enabled application development.
  • Lead technical delivery for AI use cases from discovery through production deployment.
  • Guide Full Stack AI Engineers, Automation Engineers, Dev Ops teams and vendor partners.
  • Partner with Architecture, Cybersecurity, Data, Legal, Risk, HR, Finance, Product and Business teams.

Responsibilities AI Engineering Leadership

  • Set engineering standards for enterprise AI applications, including architecture patterns, code quality, API design, observability and production readiness.
  • Lead technical design reviews for AI use cases and ensure solutions meet enterprise architecture and security expectations.
  • Create reusable blueprints for RAG, agentic AI, AI workflow orchestration, retrieval pipelines, evaluation frameworks and AI-enabled products.
  • Mentor engineers and establish a high-performance AI engineering culture focused on speed, quality and measurable business value.
  • Translate executive priorities into technical roadmaps and delivery plans.

Solution Architecture and Delivery

  • Design end-to-end AI solutions covering frontend, backend, orchestration, LLM integration, data retrieval, workflow integration, security and monitoring.
  • Lead delivery of priority use cases such as IVR/voice AI, Finance Bot, HR Shortlisting, Automated Testing Tool, AI Learning Hub and internal knowledge copilots.
  • Define when to use Azure AI Foundry, Azure OpenAI, Claude, local/open-source models, Copilot Studio, Semantic Kernel, Lang Graph or other tooling.
  • Ensure solutions are modular, API-first, maintainable and able to scale across business units and Opcos.
  • Review and approve technical design documents, solution architecture, integration patterns and deployment approaches.

LLM, RAG and Agentic AI

  • Design RAG solutions with retrieval quality, access control, source citation, chunking strategy, embeddings, vector search and grounding controls.
  • Design agentic workflows with tool calling, task planning, human-in-the-loop controls, escalation paths and guardrails.
  • Develop standards for prompt engineering, system prompts, reusable prompt libraries and prompt/version management.
  • Establish AI evaluation methods covering accuracy, hallucination risk, latency, cost, safety, relevance and user satisfaction.
  • Ensure AI outputs are explainable, auditable and aligned with responsible AI expectations.

Governance, Security and Risk

  • Embed responsible AI, data privacy, access control and model risk considerations into every AI solution.
  • Partner with Cybersecurity and Risk teams to assess threats such as prompt injection, data leakage, insecure tool use and unauthorized access.
  • Define governance checkpoints for proof of concept, pilot, production release and post-production monitoring.
  • Ensure AI solutions meet internal policies, regulatory expectations and data sovereignty requirements where applicable.
  • Maintain documentation and decision records for AI architecture, model/tool selection, risks and mitigations.

Stakeholder and Vendor Management

  • Engage business stakeholders to clarify requirements, outcomes, metrics and operational readiness.
  • Support build-vs-buy decisions and vendor assessments for AI platforms, APIs, models and development partners.
  • Guide third-party vendors to comply with internal AI engineering standards.
  • Prepare technical input for business cases, funding requests, executive reviews and steering committees.
  • Act as the technical point of accountability for priority AI delivery.

Deliverables Expected in the First 6-12 Months

  • Establish clear delivery patterns and reusable assets for priority AI use cases.
  • Support at least two to four high-priority MVPs/pilots moving from discovery into controlled pilot or production readiness.
  • Create documentation, reusable code, deployment patterns, evaluation methods and operational practices that reduce future delivery effort.
  • Demonstrate measurable improvements in delivery speed, automation, user productivity, AI quality, operational efficiency or cost avoidance.
  • Build credibility with…

Title: Lead AI Engineer

Company: Samaha Consulting

Location: Johannesburg, South Africa

Category:

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